NLPCloud.io helps devs add language processing smarts to their apps

While visual ‘no code‘ tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools — at the ‘deep tech’ AI coal face — still requires some expert help (and/or costly in-house expertise).

This is where bootstrapping French startup, NLPCloud.io, is plying a trade in MLOps/AIOps — or ‘compute platform as a service’ (being as it runs the queries on its own servers) — with a focus on natural language processing (NLP), as its name suggests.

Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP — a technology that can help businesses scale their capacity to intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. (Although it’s worth emphasizing that the bulk of NLP research has focused on the English language — meaning that’s where this tech is most mature; so associated AI advances are not universally distributed.)

Production ready (pre-trained) NLP models for English are readily available ‘out of the box’. There are also dedicated open source frameworks offering help with training models. But businesses wanting to tap into NLP still need to have the DevOps resource and chops to implement NLP models.

NLPCloud.io is catering to businesses that don’t feel up to the implementation challenge themselves — offering “production-ready NLP API” with the promise of “no DevOps required”.

Its API is based on Hugging Face and spaCy open-source models. Customers can either choose to use ready-to-use pre-trained models (it selects the “best” open source models; it does not build its own); or they can upload custom models developed internally by their own data scientists — which it says is a point of differentiation vs SaaS services such as Google Natural Language (which uses Google’s ML models) or Amazon Comprehend and Monkey Learn.

NLPCloud.io says it wants to democratize NLP by helping developers and data scientists deliver these projects “in no time and at a fair price”. (It has a tiered pricing model based on requests per minute, which starts at $39pm and ranges up to $1,199pm, at the enterprise end, for one custom model running on a GPU. It does also offer a free tier so users can test models at low request velocity without incurring a charge.)

“The idea came from the fact that, as a software engineer, I saw many AI projects fail because of the deployment to production phase,” says sole founder and CTO Julien Salinas. “Companies often focus on building accurate and fast AI models but today more and more excellent open-source models are available and are doing an excellent job… so the toughest challenge now is being able to efficiently use these models in production. It takes AI skills, DevOps skills, programming skill… which is why it’s a challenge for so many companies, and which is why I decided to launch NLPCloud.io.”

The platform launched in January 2021 and now has around 500 users, including 30 who are paying for the service. While the startup, which is based in Grenoble, in the French Alps, is a team of three for now, plus a couple of independent contractors. (Salinas says he plans to hire five people by the end of the year.)

“Most of our users are tech startups but we also start having a couple of bigger companies,” he tells TechCrunch. “The biggest demand I’m seeing is both from software engineers and data scientists. Sometimes it’s from teams who have data science skills but don’t have DevOps skills (or don’t want to spend time on this). Sometimes it’s from tech teams who want to leverage NLP out-of-the-box without hiring a whole data science team.”

“We have very diverse customers, from solo startup founders to bigger companies like BBVA, Mintel, Senuto… in all sorts of sectors (banking, public relations, market research),” he adds.

Use cases of its customers include lead generation from unstructured text (such as web pages), via named entities extraction; and sorting support tickets based on urgency by conducting sentiment analysis.

Content marketers are also using its platform for headline generation (via summarization). While text classification capabilities are being used for economic intelligence and financial data extraction, per Salinas.

He says his own experience as a CTO and software engineer working on NLP projects at a number of tech companies led him to spot an opportunity in the challenge of AI implementation.

“I realized that it was quite easy to build acceptable NLP models thanks to great open-source frameworks like spaCy and Hugging Face Transformers but then I found it quite hard to use these models in production,” he explains. “It takes programming skills in order to develop an API, strong DevOps skills in order to build a robust and fast infrastructure to serve NLP models (AI models in general consume a lot of resources), and also data science skills of course.

“I tried to look for ready-to-use cloud solutions in order to save weeks of work but I couldn’t find anything satisfactory. My intuition was that such a platform would help tech teams save a lot of time, sometimes months of work for the teams who don’t have strong DevOps profiles.”

“NLP has been around for decades but until recently it took whole teams of data scientists to build acceptable NLP models. For a couple of years, we’ve made amazing progress in terms of accuracy and speed of the NLP models. More and more experts who have been working in the NLP field for decades agree that NLP is becoming a ‘commodity’,” he goes on. “Frameworks like spaCy make it extremely simple for developers to leverage NLP models without having advanced data science knowledge. And Hugging Face’s open-source repository for NLP models is also a great step in this direction.

“But having these models run in production is still hard, and maybe even harder than before as these brand new models are very demanding in terms of resources.”

The models NLPCloud.io offers are picked for performance — where “best” means it has “the best compromise between accuracy and speed”. Salinas also says they are paying mind to context, given NLP can be used for diverse user cases — hence proposing number of models so as to be able to adapt to a given use.

“Initially we started with models dedicated to entities extraction only but most of our first customers also asked for other use cases too, so we started adding other models,” he notes, adding that they will continue to add more models from the two chosen frameworks — “in order to cover more use cases, and more languages”.

SpaCy and Hugging Face, meanwhile, were chosen to be the source for the models offered via its API based on their track record as companies, the NLP libraries they offer and their focus on production-ready framework — with the combination allowing NLPCloud.io to offer a selection of models that are fast and accurate, working within the bounds of respective trade-offs, according to Salinas.

“SpaCy is developed by a solid company in Germany called Explosion.ai. This library has become one of the most used NLP libraries among companies who want to leverage NLP in production ‘for real’ (as opposed to academic research only). The reason is that it is very fast, has great accuracy in most scenarios, and is an opinionated” framework which makes it very simple to use by non-data scientists (the tradeoff is that it gives less customization possibilities),” he says.

Hugging Face is an even more solid company that recently raised $40M for a good reason: They created a disruptive NLP library called ‘transformers’ that improves a lot the accuracy of NLP models (the tradeoff is that it is very resource intensive though). It gives the opportunity to cover more use cases like sentiment analysis, classification, summarization… In addition to that, they created an open-source repository where it is easy to select the best model you need for your use case.”

While AI is advancing at a clip within certain tracks — such as NLP for English — there are still caveats and potential pitfalls attached to automating language processing and analysis, with the risk of getting stuff wrong or worse. AI models trained on human-generated data have, for example, been shown reflecting embedded biases and prejudices of the people who produced the underlying data.

Salinas agrees NLP can sometimes face “concerning bias issues”, such as racism and misogyny. But he expresses confidence in the models they’ve selected.

“Most of the time it seems [bias in NLP] is due to the underlying data used to trained the models. It shows we should be more careful about the origin of this data,” he says. “In my opinion the best solution in order to mitigate this is that the community of NLP users should actively report something inappropriate when using a specific model so that this model can be paused and fixed.”

“Even if we doubt that such a bias exists in the models we’re proposing, we do encourage our users to report such problems to us so we can take measures,” he adds.

 

#amazon, #api, #artificial-intelligence, #artificial-neural-networks, #bbva, #computing, #developer, #devops, #europe, #germany, #google, #hugging-face, #ml, #natural-language-processing, #nlpcloud-io, #public-relations, #software-development, #speech-recognition, #startups, #transformer

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Aporia raises $5M for its AI observability platform

Machine learning (ML) models are only as good as the data you feed them. That’s true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it’s imperative that businesses monitor their data pipelines for these systems.

That’s where tools like Aporia come in. The Tel Aviv-based company today announced that it has raised a $5 million seed round for its monitoring platform for ML models. The investors are Vertex Ventures and TLV Partners.

Image Credits: Aporia

Aporia co-founder and CEO Liran Hason, after five years with the Israel Defense Forces, previously worked on the data science team at Adallom, a security company that was acquired by Microsoft in 2015. After the sale, he joined venture firm Vertex Ventures before starting Aporia in late 2019. But it was during his time at Adallom where he first encountered the problems that Aporio is now trying to solve.

“I was responsible for the production architecture of the machine learning models,” he said of his time at the company. “So that’s actually where, for the first time, I got to experience the challenges of getting models to production and all the surprises that you get there.”

The idea behind Aporia, Hason explained, is to make it easier for enterprises to implement machine learning models and leverage the power of AI in a responsible manner.

“AI is a super powerful technology,” he said. “But unlike traditional software, it highly relies on the data. Another unique characteristic of AI, which is very interesting, is that when it fails, it fails silently. You get no exceptions, no errors. That becomes really, really tricky, especially when getting to production, because in training, the data scientists have full control of the data.”

But as Hason noted, a production system may depend on data from a third-party vendor and that vendor may one day change the data schema without telling anybody about it. At that point, a model — say for predicting whether a bank’s customer may default on a loan — can’t be trusted anymore, but it may take weeks or months before anybody notices.

Aporia constantly tracks the statistical behavior of the incoming data and when that drifts too far away from the training set, it will alert its users.

One thing that makes Aporio unique is that it gives its users an almost IFTTT or Zapier-like graphical tool for setting up the logic of these monitors. It comes pre-configured with more than 50 combinations of monitors and provides full visibility in how they work behind the scenes. That, in turn, allows businesses to fine-tune the behavior of these monitors for their own specific business case and model.

Initially, the team thought it could build generic monitoring solutions. But the team realized that this wouldn’t only be a very complex undertaking, but that the data scientists who build the models also know exactly how those models should work and what they need from a monitoring solution.

“Monitoring production workloads is a well-established software engineering practice, and it’s past time for machine learning to be monitored at the same level,” said Rona Segev, founding partner at  TLV Partners. “Aporia‘s team has strong production-engineering experience, which makes their solution stand out as simple, secure and robust.”

 

#adallom, #aporia, #artificial-intelligence, #enterprise, #machine-learning, #microsoft, #ml, #recent-funding, #startups, #tc, #tel-aviv, #tlv-partners, #vertex-ventures

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5 machine learning essentials non-technical leaders need to understand

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus, head of developer and ML platform at Square, describes as “the democratization of ML.” It’s become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.

But for engineering and team leaders without an ML background, this can also feel overwhelming and intimidating. I regularly meet smart, successful, highly competent and normally very confident leaders who struggle to navigate a constructive or effective conversation on ML — even though some of them lead teams that engineer it.

I’ve spent more than two decades in the ML space, including work at Apple to build the world’s largest online app and music store. As the senior director of engineering, anti-evil, at Reddit, I used ML to understand and combat the dark side of the web.

For this piece, I interviewed a select group of successful ML leaders including Sambhus; Lior Gavish, co-founder at Monte Carlo; and Yotam Hadass, VP of engineering at Electric.ai, for their insights. I’ve distilled our best practices and must-know components into five practical and easily applicable lessons.

1. ML recruiting strategy

Recruiting for ML comes with several challenges.

The first is that it can be difficult to differentiate machine learning roles from more traditional job profiles (such as data analysts, data engineers and data scientists) because there’s a heavy overlap between descriptions.

Secondly, finding the level of experience required can be challenging. Few people in the industry have substantial experience delivering production-grade ML (for instance, you’ll sometimes notice resumes that specify experience with ML models but then find their models are rule-based engines rather than real ML models).

When it comes to recruiting for ML, hire experts when you can, but also look into how training can help you meet your talent needs. Consider upskilling your current team of software engineers into data/ML engineers or hire promising candidates and provide them with an ML education.

machine learning essentials for leaders

Image Credits: Snehal Kundalkar

The other effective way to overcome these recruiting challenges is to define roles largely around:

  • Product: Look for candidates with a technical curiosity and a strong business/product sense. This framework is often more important than the ability to apply the most sophisticated models.
  • Data: Look for candidates that can help select models, design features, handle data modeling/vectorization and analyze results.
  • Platform/Infrastructure: Look for people who evaluate/integrate/build platforms to significantly accelerate the productivity of data and engineering teams; extract, transform, load (ETLs); warehouse infrastructures; and CI/CD frameworks for ML.

    #artificial-intelligence, #column, #ec-future-of-work, #ec-how-to, #engineer, #machine-learning, #ml, #startups

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OctoML raises $28M Series B for its machine learning acceleration platform

OctoML, a Seattle-based startup that offers a machine learning acceleration platform build on top of the open-source Apache TVM compiler framework project, today announced that it has raised a $28 million Series B funding round led by Addition Captial. Previous investors Madrona Venture Group and Amplify Partners also participated in this round, which brings the company’s total funding to $47 million. The company last raised in April 2020, when it announced its $15 million Series A round led by Amplify

The promise of OctoML is that developers can bring their models to its platform and the service will automatically optimize that model’s performance for any given cloud or edge device. The founding team created the TVM project, which

As Brazil-born OctoML co-founder and CEO Luis Ceze told me, since raising its Series A round, the company started onboarding some early adopters to its ‘Octomizer’ SaaS platform.

Image Credits: OctoML

“It’s still in early access, but we are we have close to 1,000 early access sign-ups on the waitlist,” Ceze said. “That was a pretty strong signal for us to end up taking this [funding]. The Series B was pre-emptive. We were planning on starting to raise money right about now. We had barely started spending our Series A money — we still had a lot of that left. But since we saw this growth and we had more paying customers than we anticipated, there were a lot of signals like, ‘hey, now we can accelerate the go-to-market machinery, build a customer success team and continue expanding the engineering team to build new features.”

Ceze tells me that the team also saw strong growth signals in the overall community around the TVM project (with about 1,000 people attending its virtual conference last year). As for its customer base (and companies on its waitlist), Ceze says it represents a wide range of verticals that range from defense contractors to financial services and life science companies, automotive firms and startups in a variety of fields.

Recently, OctoML also launched support for the Apple M1 chip — and saw very good performance from that.

The company has also formed partnerships with industry heavyweights like Microsoft (which is also a customer), Qualcomm, AMD and Sony to build out the open-source components and optimize its service for an even wider range of models (and larger ones, too).

On the engineering side, Ceze tells me that the team is looking at not just optimizing and tuning models but also the training process. Training ML models can quickly become costly and any service that can speed up that process leads to direct savings for its users — which in turn makes OctoML an easier sell. The plan here, Ceze tells me, is to offer an end-to-end solution where people can optimize their ML training and the resulting models and then push their models out to their preferred platform. Right now, its users still have to take the artifact that the Octomizer creates and deploy that themselves, but deployment support is on OctoML’s roadmap.

“When we first met Luis and the OctoML team, we knew they were poised to transform the way ML teams deploy their machine learning models,” said Lee Fixel, founder of Addition. “They have the vision, the talent and the technology to drive ML transformation across every major enterprise. They launched Octomizer six months ago and it’s already becoming the go-to solution developers and data scientists use to maximize ML model performance. We look forward to supporting the company’s continued growth.”

#amd, #amplify, #amplify-partners, #artificial-intelligence, #brazil, #developer, #enterprise, #lee-fixel, #machine-learning, #madrona-venture-group, #microsoft, #ml, #octoml, #qualcomm, #recent-funding, #seattle, #series-a, #sony, #startups, #venture-capital

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Adobe delivers native Photoshop for Apple Silicon Macs and a way to enlarge images without losing detail

Adobe has been moving quickly to update its imaging software to work natively on Apple’s new in-house processors for Macs, starting with the M1-based MacBook Pro and MacBook Air released late last year. After shipping native versions of Lightroom and Camera Raw, it’s now releasing an Apple Silicon-optimized version of Photoshop, which delivers big performance gain vs. the Intel version running on Apple’s Rosetta 2 software emulation layer.

How much better? Per internal testing, Adobe says that users should see improvements of up to 1.5x faster performance on a number of different features offered by Photoshop, vs. the same tasks being done on the emulated version. That’s just the start, however, since Adobe says it’s going to continue to coax additional performance improvements out of the software on Apple Silicon in collaboration with Apple over time. Some features are also still missing from the M1-friendly addition, including the ‘Invite to Edit Cloud Documents’ and ‘Preset Syncing’ options, but those will be ported over in future iterations as well.

In addition to the Apple Silicon version of Photoshop, Adobe is also releasing a new Super Resolution feature in the Camera Raw plugin (to be released for Lightroom later) that ships with the software. This is an image enlarging feature that uses machine learning trained on a massive image dataset to blow up pictures to larger sizes while still preserving details. Adobe has previously offered a super resolution option that combined multiple exposures to boost resolution, but this works from a single photo.

It’s the classic ‘Computer, enhance’ sci-fi feature made real, and it builds on work that Photoshop previously did to introduce its ‘Enhance details’ feature. If you’re not a strict Adobe loyalist, you might also be familiar with Pixelmator Pro’s ‘ML Super Resolution’ feature, which works in much the same way – albeit using a different ML model and training data set.

Adobe's Super Resolution comparison photo

Adobe’s Super Resolution in action

The bottom line is that Adobe’s Super Resolution will output an image with twice the horizontal and twice the vertical resolution – meaning in total, it has 4x the number of pixels. It’ll do that while preserving detail and sharpness, which adds up to allowing you to make larger prints from images that previously wouldn’t stand up to that kind of enlargement. It’s also great for cropping in on photos in your collection to capture tighter shots of elements that previously would’ve been rendered blurry and disappointing as a result.

This feature benefits greatly from GPUs that are optimized for machine learning jobs, including CoreML and Windows ML. That means that Apple’s M1 chip is a perfect fit, since it includes a dedicated ML processing region called the Neural Engine. Likewise, Nvidia’s RTX series of GPUs and their TensorCores are well-suited to the task.

Adobe also released some major updates for Photoshop for iPad, including version history for its Cloud Documents non-local storage. You can also now store versions of Cloud Documents offline and edit them locally on your device.

#adobe-creative-cloud, #adobe-lightroom, #adobe-photoshop, #apple, #apple-inc, #apps, #artificial-intelligence, #imaging, #intel, #m1, #machine-learning, #macintosh, #ml, #photoshop, #pixelmator, #software, #steve-jobs, #tc

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Microsoft’s Azure Arc multi-cloud platform now supports machine learning workloads

With Azure Arc, Microsoft offers a service that allows its customers to run Azure in any Kubernetes environment, no matter where that container cluster is hosted. From Day One, Arc supported a wide range of use cases, but one feature that was sorely missing when it first launched was support for machine learning (ML). But one of the advantages of a tool like Arc is that it allows enterprises to run their workloads close to their data and today, that often means using that data to train ML models.

At its Ignite conference, Microsoft today announced that it bringing exactly this capability to Azure Arc with the addition of Azure Machine Learning to the set of Arc-enabled data services.

“By extending machine learning capabilities to hybrid and multicloud environments, customers can run training models where the data lives while leveraging existing infrastructure investments. This reduces data movement and network latency, while meeting security and compliance requirements,” Azure GM Arpan Shah writes in today’s announcement.

This new capability is now available to Arc customers.

In addition to bringing this new machine learning capability to Arc, Microsoft also today announced that Azure Arc enabled Kubernetes, which allows users to deploy standard Kubernetes configurations to their clusters anywhere, is now generally available.

Also new in this world of hybrid Azure services is support for Azure Kubernetes Service on Azure Stack HCI. That’s a mouthful, but Azure Stack HCI is Microsoft’s platform for running Azure on a set of standardized, hyperconverged hardware inside a customer’s datacenter. The idea pre-dates Azure Arc, but it remains a plausible alternative for enterprises who want to run Azure in their own data center and has continued support from vendors like Dell, Lenovo, HPE, Fujitsu and DataOn.

On the open-source side of Arc, Microsoft also today stressed that Arc is built to work with any Kubernetes distribution that is conformant to the standard of the Cloud Native Computing Foundation (CNCF) and that it has worked with RedHat, Canonical, Rancher and now Nutanix to test and validate their Kubernetes implementations on Azure Arc.

#cloud-computing, #cloud-infrastructure, #cloud-native-computing-foundation, #computing, #dell, #fujitsu, #hpe, #kubernetes, #lenovo, #machine-learning, #microsoft, #microsoft-ignite-2021, #microsoft-azure, #ml, #nutanix, #red-hat, #redhat, #tc

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NeuReality raises $8M for its novel AI inferencing platform

NeuReality, an Israeli AI hardware startup that is working on a novel approach to improving AI inferencing platforms by doing away with the current CPU-centric model, is coming out of stealth today and announcing an $8 million seed round. The group of investors includes Cardumen Capital, crowdfunding platform OurCrowd and Varana Capital. The company also today announced that Naveen Rao, the GM of Intel’s AI Products Group and former CEO of Nervana System (which Intel acquired), is joining the company’s board of directors.

The founding team, CEO Moshe Tanach, VP of operations Tzvika Shmueli and VP for very large-scale integration Yossi Kasus, has a background in AI but also networking, with Tanach spending time at Marvell and Intel, for example, Shmueli at Mellanox and Habana Labs and Kasus at Mellanox, too.

It’s the team’s networking and storage knowledge and seeing how that industry built its hardware that now informs how NeuReality is thinking about building its own AI platform. In an interview ahead of today’s announcement, Tanach wasn’t quite ready to delve into the details of NeuReality’s architecture, but the general idea here is to build a platform that will allow hyperscale clouds and other data center owners to offload their ML models to a far more performant architecture where the CPU doesn’t become a bottleneck.

“We kind of combined a lot of techniques that we brought from the storage and networking world,” Tanach explained. Think about traffic manager and what it does for Ethernet packets. And we applied it to AI. So we created a bottom-up approach that is built around the engine that you need. Where today, they’re using neural net processors — we have the next evolution of AI computer engines.”

As Tanach noted, the result of this should be a system that — in real-world use cases that include not just synthetic benchmarks of the accelerator but also the rest of the overall architecture — offer 15 times the performance per dollar for basic deep learning offloading and far more once you offload the entire pipeline to its platform.

NeuReality is still in its early days, and while the team has working prototypes now, based on a Xilinx FPGA, it expects to be able to offer its fully custom hardware solution early next year. As its customers, NeuReality is targeting the large cloud providers, but also data center and software solutions providers like WWT to help them provide specific vertical solutions for problems like fraud detection, as well as OEMs and ODMs.

Tanach tells me that the team’s work with Xilinx created the groundwork for its custom chip — though building that (and likely on an advanced node), will cost money, so he’s already thinking about raising the next round of funding for that.

“We are already consuming huge amounts of AI in our day-to-day life and it will continue to grow exponentially over the next five years,” said Tanach. “In order to make AI accessible to every organization, we must build affordable infrastructure that will allow innovators to deploy AI-based applications that cure diseases, improve public safety and enhance education. NeuReality’s technology will support that growth while making the world smarter, cleaner and safer for everyone. The cost of the AI infrastructure and AIaaS will no longer be limiting factors.”

NeuReality team. Photo credit - NeuReality

Image Credits: NeuReality

#artificial-intelligence, #cardumen-capital, #computing, #ethernet, #fpga, #funding, #fundings-exits, #habana-labs, #hardware-startup, #intel, #mellanox, #ml, #neureality, #nvidia, #ourcrowd, #recent-funding, #science-and-technology, #startups, #tc, #technology, #varana-capital, #xilinx

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IPRally is building a knowledge graph-based search engine for patents

IPRally, a burgeoning startup out of Finland aiming to solve the patent search problem, has raised €2 million in seed funding.

Leading the round is by JOIN Capital, and Spintop Ventures, with participation from existing pre-seed backer Icebreaker VC. It brings the total raised by the 2018-founded company to €2.35 million.

Co-founded by CEO Sakari Arvela, who has 15 years experience as a patent attorney, IPRally has built a knowledge graph to help machines better understand the technical details of patents and to enable humans to more efficiently trawl through existing patients. The premise is that a graph-based approach is more suited to patent search than simple keywords or freeform text search.

That’s because, argues Arvela, every patent publication can be distilled down to a simpler knowledge graph that “resonates” with the way IP professionals think and is infinitely more machine readable.

“We founded IPRally in April 2018, after one year of bootstrapping and proof-of-concepting with my co-founder and CTO Juho Kallio,” he tells me. “Before that, I had digested the graph approach myself for about two years and collected the courage to start the venture”.

Arvela says patent search is a hard problem to solve since it involves both deep understanding of technology and the capability to compare different technologies in detail.

“This is why this has been done almost entirely manually for as long as the patent system has existed. Even the most recent out-of-the-box machine learning models are way too inaccurate to solve the problem. This is why we have developed a specific ML model for the patent domain that reflects the way human professionals approach the search task and make the problem sensible for the computers too”.

That approach appears to be paying off, with IPRally already being used by customers such as Spotify and ABB, as well as intellectual property offices. Target customers are described as any corporation that actively protects its own R&D with patents and has to navigate the IPR landscape of competitors.

Meanwhile, IPRally is not without its own competition. Arvela cites industry giants like Clarivate and Questel that dominate the market with traditional keyword search engines.

In addition, there are a few other AI-based startups, like Amplified and IPScreener. “IPRally’s graph approach makes the searches much more accurate, allows detail-level computer analysis, and offer a non-black-box solution that is explainable for and controllable by the user,” he adds.

#europe, #finland, #fundings-exits, #iprally, #machine-learning, #ml, #patent, #patent-law, #patent-search, #startups, #tc

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Tips for applying an intersectional framework to AI development

By now, most of us in tech know that the inherent bias we possess as humans creates an inherent bias in AI applications — applications that have become so sophisticated they’re able to shape the nature of our everyday lives and even influence our decision-making.

The more prevalent and powerful AI systems become, the sooner the industry must address questions like: What can we do to move away from using AI/ML models that demonstrate unfair bias?

How can we apply an intersectional framework to build AI for all people, knowing that different individuals are affected by and interact with AI in different ways based on the converging identities they hold?

Start with identifying the variety of voices that will interact with your model.

Intersectionality: What it means and why it matters

Before tackling the tough questions, it’s important to take a step back and define “intersectionality.” A term defined by Kimberlé Crenshaw, it’s a framework that empowers us to consider how someone’s distinct identities come together and shape the ways in which they experience and are perceived in the world.

This includes the resulting biases and privileges that are associated with each distinct identity. Many of us may hold more than one marginalized identity and, as a result, we’re familiar with the compounding effect that occurs when these identities are layered on top of one another.

At The Trevor Project, the world’s largest suicide prevention and crisis intervention organization for LGBTQ youth, our chief mission is to provide support to each and every LGBTQ young person who needs it, and we know that those who are transgender and nonbinary and/or Black, Indigenous, and people of color face unique stressors and challenges.

So, when our tech team set out to develop AI to serve and exist within this diverse community — namely to better assess suicide risk and deliver a consistently high quality of care — we had to be conscious of avoiding outcomes that would reinforce existing barriers to mental health resources like a lack of cultural competency or unfair biases like assuming someone’s gender based on the contact information presented.

Though our organization serves a particularly diverse population, underlying biases can exist in any context and negatively impact any group of people. As a result, all tech teams can and should aspire to build fair, intersectional AI models, because intersectionality is the key to fostering inclusive communities and building tools that serve people from all backgrounds more effectively.

Doing so starts with identifying the variety of voices that will interact with your model, in addition to the groups for which these various identities overlap. Defining the opportunity you’re solving is the first step because once you understand who is impacted by the problem, you can identify a solution. Next, map the end-to-end experience journey to learn the points where these people interact with the model. From there, there are strategies every organization, startup and enterprise can apply to weave intersectionality into every phase of AI development — from training to evaluation to feedback.

Datasets and training

The quality of a model’s output relies on the data on which it’s trained. Datasets can contain inherent bias due to the nature of their collection, measurement and annotation — all of which are rooted in human decision-making. For example, a 2019 study found that a healthcare risk-prediction algorithm demonstrated racial bias because it relied on a faulty dataset for determining need. As a result, eligible Black patients received lower risk scores in comparison to white patients, ultimately making them less likely to be selected for high-risk care management.

Fair systems are built by training a model on datasets that reflect the people who will be interacting with the model. It also means recognizing where there are gaps in your data for people who may be underserved. However, there’s a larger conversation to be had about the overall lack of data representing marginalized people — it’s a systemic problem that must be addressed as such, because sparsity of data can obscure both whether systems are fair and whether the needs of underrepresented groups are being met.

To start analyzing this for your organization, consider the size and source of your data to identify what biases, skews or mistakes are built-in and how the data can be improved going forward.

The problem of bias in datasets can also be addressed by amplifying or boosting specific intersectional data inputs, as your organization defines it. Doing this early on will inform your model’s training formula and help your system stay as objective as possible — otherwise, your training formula may be unintentionally optimized to produce irrelevant results.

At The Trevor Project, we may need to amplify signals from demographics that we know disproportionately find it hard to access mental health services, or for demographics that have small sample sizes of data compared to other groups. Without this crucial step, our model could produce outcomes irrelevant to our users.

Evaluation

Model evaluation is an ongoing process that helps organizations respond to ever-changing environments. Evaluating fairness began with looking at a single dimension — like race or gender or ethnicity. The next step for the tech industry is figuring out how to best compare intersectional groupings to evaluate fairness across all identities.

To measure fairness, try defining intersectional groups that could be at a disadvantage and the ones that may have an advantage, and then examine whether certain metrics (for example, false-negative rates) vary among them. What do these inconsistencies tell you? How else can you further examine which groups are underrepresented in a system and why? These are the kinds of questions to ask at this phase of development.

Developing and monitoring a model based on the demographics it serves from the start is the best way for organizations to achieve fairness and alleviate unfair bias. Based on the evaluation outcome, a next step might be to purposefully overserve statistically underrepresented groups to facilitate training a model that minimizes unfair bias. Since algorithms can lack impartiality due to societal conditions, designing for fairness from the outset helps ensure equal treatment of all groups of individuals.

Feedback and collaboration

Teams should also have a diverse group of people involved in developing and reviewing AI products — people who are diverse not only in identities, but also in skillset, exposure to the product, years of experience and more. Consult stakeholders and those who are impacted by the system for identifying problems and biases.

Lean on engineers when brainstorming solutions. For defining intersectional groupings, at The Trevor Project, we worked across the teams closest to our crisis-intervention programs and the people using them — like Research, Crisis Services and Technology. And reach back out to stakeholders and people interacting with the system to collect feedback upon launch.

Ultimately, there isn’t a “one-size-fits-all” approach to building intersectional AI. At The Trevor Project, our team has outlined a methodology based on what we do, what we know today and the specific communities we serve. This is not a static approach and we remain open to evolving as we learn more. While other organizations may take a different approach to build intersectional AI, we all have a moral responsibility to construct fairer AI systems, because AI has the power to highlight — and worse, magnify — the unfair biases that exist in society.

Depending on the use case and community in which an AI system exists, the magnification of certain biases can result in detrimental outcomes for groups of people who may already face marginalization. At the same time, AI also has the ability to improve quality of life for all people when developed through an intersectional framework. At The Trevor Project, we strongly encourage tech teams, domain experts and decision-makers to think deeply about codifying a set of guiding principles to initiate industry-wide change — and to ensure future AI models reflect the communities they serve.

#artificial-intelligence, #bias, #column, #cybernetics, #developer, #diversity, #ml, #risk

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AI’s next act: Genius chips, programmable silicon and the future of computing

If only 10% of the world had enough power to run a cell phone, would mobile have changed the world in the way that it did?

It’s often said the future is already here — just not evenly distributed. That’s especially true in the world of artificial intelligence (AI) and machine learning (ML). Many powerful AI/ML applications already exist in the wild, but many also require enormous computational power — often at scales only available to the largest companies in existence or entire nation-states. Compute-heavy technologies are also hitting another roadblock: Moore’s law is plateauing and the processing capacity of legacy chip architectures are running up against the limits of physics.

If major breakthroughs in silicon architecture efficiency don’t happen, AI will suffer an unevenly distributed future and huge swaths of the population miss out on the improvements AI could make to their lives.

The next evolutionary stage of technology depends on completing the transformation that will make silicon architecture as flexible, efficient and ultimately programmable as the software we know today. If we cannot take major steps to provide easy access to ML we’ll lose unmeasurable innovation by having only a few companies in control of all the technology that matters. So what needs to change, how fast is it changing and what will that mean for the future of technology?

An inevitable democratization of AI: A boon for startups and smaller businesses

If you work at one of the industrial giants (including those “outside” of tech), congratulations — but many of the problems with current AI/ML computing capabilities I present here may not seem relevant.

For those of you working with lesser caches of resources, whether financially or talent-wise, view the following predictions as the herald of a new era in which organizations of all sizes and balance sheets have access to the same tiers of powerful AI and ML-powered software. Just like cell phones democratized internet access, we see a movement in the industry today to put AI in the hands of more and more people.

Of course, this democratization must be fueled by significant technological advancement that actually makes AI more accessible — good intentions are not enough, regardless of the good work done by companies like Intel and Google. Here are a few technological changes we’ll see that will make that possible.

From dumb chip to smart chip to “genius” chip

For a long time, raw performance was the metric of importance for processors. Their design reflected this. As software rose in ubiquity, processors needed to be smarter: more efficient and more commoditized, so specialized processors like GPUs arose — “smart” chips, if you will.

Those purpose-built graphics processors, by happy coincidence, proved to be more useful than CPUs for deep learning functions, and thus the GPU became one of the key players in modern AI and ML. Knowing this history, the next evolutionary step becomes obvious: If we can purpose-build hardware for graphics applications, why not for specific deep learning, AI and ML?

There’s also a unique confluence of factors that makes the next few years pivotal for chipmaking and tech in general. First and second, we’re seeing a plateauing of Moore’s law (which predicts a doubling of transistors on integrated circuits every two years) and the end of Dennard scaling (which says performance-per-watt doubles at about the same rate). Together, that used to mean that for any new generation of technology, chips doubled in density and increased in processing power while drawing the same amount of power. But we’ve now reached the scale of nanometers, meaning we’re up against the limitations of physics.

Thirdly, compounding the physical challenge, the computing demands of next-gen AI and ML apps are beyond what we could have imagined. Training neural networks to within even a fraction of human image recognition, for example, is surprisingly hard and takes huge amounts of processing power. The most intense applications of machine learning are things like natural language processing (NLP), recommender systems that deal with billions or trillions of possibilities, or super high-resolution computer vision, as is used in the medical and astronomical fields.

Even if we could have predicted we’d have to create and train algorithmic brains to learn how to speak human language or identify objects in deep space, we still could not have guessed just how much training — and therefore processing power — they might need to become truly useful and “intelligent” models.

Of course, many organizations are performing these sorts of complex ML applications. But these sorts of companies are usually business or scientific leaders with access to huge amounts of raw computing power and the talent to understand and deploy them. All but the largest enterprises are locked out of the upper tiers of ML and AI.

That’s why the next generation of smart chips — call them “genius” chips — will be about efficiency and specialization. Chip architecture will be made to optimize for the software running on it and run altogether more efficiently. When using high-powered AI doesn’t take a whole server farm and becomes accessible to a much larger percentage of the industry, the ideal conditions for widespread disruption and innovation become real. Democratizing expensive, resource intensive AI goes hand-in-hand with these soon-to-be-seen advances in chip architecture and software-centered hardware design.

A renewed focus on future-proofing innovation

The nature of AI creates a special challenge for the creators and users of AI hardware. The amount of change itself is huge: We’re living through the leap from humans writing code to software 2.0 — where engineers can train machine learning programs to eventually “run themselves.” The rate of change is also unprecedented: ML models can be obsolete in months or even weeks, and the very methods through which training happens are in constant evolution.

But creating new AI hardware products still requires designing, prototyping, calibrating, troubleshooting, production and distribution. It can take two years from concept to product-in-hand. Software has, of course, always outpaced hardware development, but now the differential in velocity is irreconcilable. We need to be more clever about the hardware we create for a future we increasingly cannot predict.

In fact, the generational way we think about technology is beginning to break down. When it comes to ML and AI, hardware must be built with the expectation that much of what we know today will be obsolete by the time we have the finished product. Flexibility and customization will be the key attributes of successful hardware in the age of AI, and I believe this will be a further win for entire market.

Instead of sinking resources into the model du jour or a specific algorithm, companies looking to take advantage of these technologies will have more options for processing stacks that can evolve and change as the demands of ML and AI models evolve and change.

This will allow companies of all sizes and levels of AI savvy to stay creative and competitive for longer and prevent the stagnation that can occur when software is limited by hardware — all leading to more interesting and unexpected AI applications for more organizations.

Widespread adoption of real AI and ML technologies

I’ll be the first to admit to tech’s fascination with shiny objects. There was a day when big data was the solution to everything and IoT was to be the world’s savior. AI has been through the hype cycle in the same way (arguably multiple times). Today, you’d be hard pressed to find a tech company that doesn’t purport to use AI in some way, but chances are they are doing something very rudimentary that’s more akin to advanced analytics.

It’s my firm belief that the AI revolution we’ve all been so excited about simply has not happened yet. In the next two to three years however, as the hardware that enables “real” AI power makes its way into more and more hands, it will happen. As far as predicting the change and disruption that will come from widespread access to the upper echelons of powerful ML and AI — there are few ways to make confident predictions, but that is exactly the point!

Much like cellphones put so much power in the hands of regular people everywhere, with no barriers to entry either technical or financial (for the most part), so will the coming wave of software-defined hardware that is flexible, customizable and future-proof. The possibilities are truly endless, and it will mark an important turning point in technology. The ripple effects of AI democratization and commoditization will not stop with just technology companies, and so even more fields stand to be blown open as advanced, high-powered AI becomes accessible and affordable.

Much of the hype around AI — all the disruption it was supposed to bring and the leaps it was supposed to fuel — will begin in earnest in the next few years. The technology that will power it is being built as we speak or soon to be in the hands of the many people in the many industries who will use their newfound access as a springboard to some truly amazing advances. We’re especially excited to be a part of this future, and look forward to all the progress it will bring.

#artificial-general-intelligence, #artificial-intelligence, #column, #hardware, #machine-learning, #ml, #natural-language-processing, #neural-networks, #science

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AWS launches Trainium, its new custom ML training chip

At its annual re:Invent developer conference, AWS today announced the launch of AWS Trainium, the company’s next-gen custom chip dedicated to training machine learning models. The company promises that it can offer higher performance than any of its competitors in the cloud, with support for TensorFlow, PyTorch and MXNet.

It will be available as EC2 instances and inside Amazon SageMaker, the company’s machine learning platform.

New instances based on these custom chips will launch next year.

The main arguments for these custom chips are speed and cost. AWS promises 30% higher throughput and 45% lower cost-per-inference compared to the standard AWS GPU instances.

In addition, AWS is partnering with Intel to launch Habana Gaudi-based EC2 instances for machine learning training. Coming next year, these instances promise to offer up to 40% better price/performance compared to the current set of GPU-based EC2 instances for machine learning. These chips will support TensorFlow and PyTorch.

These new chips will make their debut in the AWS cloud in the first half of 2021.

Both of these new offerings complement AWS Inferentia, which the company launched at last year’s re:Invent. Inferentia is the inferencing counterpart to these machine learning pieces, which also uses a custom chip.

Trainium, it’s worth noting, will use the same SDK as Inferentia.

“While Inferentia addressed the cost of inference, which constitutes up to 90% of ML infrastructure costs, many development teams are also limited by fixed ML training budgets,” the AWS team writes. “This puts a cap on the scope and frequency of training needed to improve their models and applications. AWS Trainium addresses this challenge by providing the highest performance and lowest cost for ML training in the cloud. With both Trainium and Inferentia, customers will have an end-to-end flow of ML compute from scaling training workloads to deploying accelerated inference.”

#amazon, #amazon-sagemaker, #amazon-web-services, #artificial-intelligence, #aws, #aws-reinvent-2020, #cloud, #cloud-computing, #cloud-infrastructure, #computing, #deep-learning, #intel, #machine-learning, #ml, #tc, #tensorflow

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Abacus.AI raises another $22M and launches new AI modules

AI startup RealityEngines.AI changed its name to Abacus.AI in July. At the same time, it announced a $13 million Series A round. Today, only a few months later, it is not changing its name again, but it is announcing a $22 million Series B round, led by Coatue, with Decibel Ventures and Index Partners participating as well. With this, the company, which was co-founded by former AWS and Google exec Bindu Reddy, has now raised a total of $40.3 million.

Abacus co-founder Bindu Reddy, Arvind Sundararajan and Siddartha Naidu. Image Credits: Abacus.AI

In addition to the new funding, Abacus.AI is also launching a new product today, which it calls Abacus.AI Deconstructed. Originally, the idea behind RealityEngines/Abacus.AI was to provide its users with a platform that would simplify building AI models by using AI to automatically train and optimize them. That hasn’t changed, but as it turns out, a lot of (potential) customers had already invested into their own workflows for building and training deep learning models but were looking for help in putting them into production and managing them throughout their lifecycle.

“One of the big pain points [businesses] had was, ‘look, I have data scientists and I have my models that I’ve built in-house. My data scientists have built them on laptops, but I don’t know how to push them to production. I don’t know how to maintain and keep models in production.’ I think pretty much every startup now is thinking of that problem,” Reddy said.

Image Credits: Abacus.AI

Since Abacus.AI had already built those tools anyway, the company decided to now also break its service down into three parts that users can adapt without relying on the full platform. That means you can now bring your model to the service and have the company host and monitor the model for you, for example. The service will manage the model in production and, for example, monitor for model drift.

Another area Abacus.AI has long focused on is model explainability and de-biasing, so it’s making that available as a module as well, as well as its real-time machine learning feature store that helps organizations create, store and share their machine learning features and deploy them into production.

As for the funding, Reddy tells me the company didn’t really have to raise a new round at this point. After the company announced its first round earlier this year, there was quite a lot of interest from others to also invest. “So we decided that we may as well raise the next round because we were seeing adoption, we felt we were ready product-wise. But we didn’t have a large enough sales team. And raising a little early made sense to build up the sales team,” she said.

Reddy also stressed that unlike some of the company’s competitors, Abacus.AI is trying to build a full-stack self-service solution that can essentially compete with the offerings of the big cloud vendors. That — and the engineering talent to build it — doesn’t come cheap.

Image Credits: Abacus.AI

It’s no surprise then that Abacus.AI plans to use the new funding to increase its R&D team, but it will also increase its go-to-market team from two to ten in the coming months. While the company is betting on a self-service model — and is seeing good traction with small- and medium-sized companies — you still need a sales team to work with large enterprises.

Come January, the company also plans to launch support for more languages and more machine vision use cases.

“We are proud to be leading the Series B investment in Abacus.AI, because we think that Abacus.AI’s unique cloud service now makes state-of-the-art AI easily accessible for organizations of all sizes, including start-ups. Abacus.AI’s end-to-end autonomous AI service powered by their Neural Architecture Search invention helps organizations with no ML expertise easily deploy deep learning systems in production.”

 

#artificial-general-intelligence, #artificial-intelligence, #bindu-reddy, #cloud, #cloud-computing, #co-founder, #coatue, #enterprise, #entrepreneurship, #funding, #fundings-exits, #learning, #machine-learning, #ml, #recent-funding, #science-and-technology, #start-ups, #startups

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AI-tool maker Seldon raises £7.1M Series A from AlbionVC and Cambridge Innovation Capital

Seldon is a U.K. startup that specializes in the rarified world of development tools to optimize Machine Learning. What does this mean? Well, dear reader, it means that the “AI” that companies are so fond of trumpeting, does actually end up working.

It’s now raised a £7.1M Series A round co-led by AlbionVC and Cambridge Innovation Capital . The round also includes significant participation from existing investors Amadeus Capital Partners and Global Brain, with follow-on investment from other existing shareholders. The £7.1M funding will be used to accelerate R&D and drive commercial expansion, take Seldon Deploy – a new enterprise solution – to market, and double the size of the team over the next 18 months.

More accurately, Seldon is a cloud-agnostic Machine Learning (ML) deployment specialist which works in partnership with industry leaders such as Google, Red Hat, IBM and Amazon Web Services.

Key to its success is that its open-source project Seldon Core has over 700,000 models deployed to date, drastically reducing friction for users deploying ML models. The startup says its customers are getting productivity gains of as much as 92% as a result of utilizing Seldon’s product portfolio.

Alex Housley, CEO and founder of Seldon said: Speaking to TechCrunch, Housley explained that companies are using machine learning across thousands of use cases today, “but the model actually only generates real value when it’s actually running inside a real-world application.”

“So what we’ve seen emerge over these last few years are companies that specialize in specific parts of the machine learning pipeline, such as training version control features. And in our case we’re focusing on deployment. So what this means is that organizations can now build a fully bespoke AI platform that suits their needs, so they can gain a competitive advantage,” he said.

In addition, he said Seldon’s Open Source model means that companies are not locked-in: “They want to avoid locking as well they want to use tools from various different vendors. So this kind of intersection between machine learning, DevOps and cloud-native tooling is really accelerating a lot of innovation across enterprise and also within startups and growth-stage companies.”

Nadine Torbey, Investor AlbionVC added: “Seldon is at the forefront of the next wave of tech innovation, and the leadership team are true visionaries. Seldon has been able to build an impressive open-source community and add immediate productivity value to some of the world’s leading companies.”

Vin Lingathoti, Partner at Cambridge Innovation Capital said: “Machine learning has rapidly shifted from a nice-to-have to a must-have for enterprises across all industries. Seldon’s open-source platform operationalizes ML model development and accelerates the time-to-market by eliminating the pain points involved in developing, deploying and monitoring Machine Learning models at scale.”

#albionvc, #amadeus-capital-partners, #amazon-web-services, #artificial-intelligence, #cambridge-innovation-capital, #cloud-computing, #cloud-infrastructure, #cybernetics, #europe, #google, #ibm, #learning, #machine-learning, #ml, #partner, #recent-funding, #red-hat, #seldon, #startups, #tc, #united-kingdom

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Arrikto raises $10M for its MLOps platform

Arrikto, a startup that wants to speed up the machine learning development lifecycle by allowing engineers and data scientists to treat data like code, is coming out of stealth today and announcing a $10 million Series A round. The round was led by Unusual Ventures, with Unusual’s John Vrionis joining the board.

“Our technology at Arrikto helps companies overcome the complexities of implementing and managing machine learning applications,” Arrikto CEO and co-founder Constantinos Venetsanopoulos explained. “We make it super easy to set up end-to-end machine learning pipelines. More specifically, we make it easy to build, train, deploy ML models into production using Kubernetes and intelligent intelligently manage all the data around it.”

Like so many developer-centric platforms today, Arrikto is all about “shift left.” Currently, the team argues, machine learning teams and developer teams don’t speak the same language and use different tools to build models and to put them into production.

Image Credits: Arrikto

“Much like DevOps shifted deployment left, to developers in the software development life cycle, Arrikto shifts deployment left to data scientists in the machine learning life cycle,” Venetsanopoulos explained.

Arrikto also aims to reduce the technical barriers that still make implementing machine learning so difficult for most enterprises. Venetsanopoulos noted that just like Kubernetes showed businesses what a simple and scalable infrastructure could look like, Arrikto can show them what a simpler ML production pipeline can look like — and do so in a Kubernetes-native way.

Arrikto CEO Constantinos Venetsanopoulos. Image Credits: Arrikto

At the core of Arrikto is Kubeflow, the Google -incubated open-source machine learning toolkit for Kubernetes — and in many ways, you can think of Arrikto as offering an enterprise-ready version of Kubeflow. Among other projects, the team also built MiniKF to run Kubeflow on a laptop and uses Kale, which lets engineers build Kubeflow pipelines from their JupyterLab notebooks.

As Venetsanopoulos noted, Arrikto’s technology does three things: it simplifies deploying and managing Kubeflow, allows data scientists to manage it using the tools they already know, and it creates a portable environment for data science that enables data versioning and data sharing across teams and clouds.

While Arrikto has stayed off the radar since it launched out of Athens, Greece in 2015, the founding team of Venetsanopoulos and CTO Vangelis Koukis already managed to get a number of large enterprises to adopt its platform. Arrikto currently has more than 100 customers and, while the company isn’t allowed to name any of them just yet, Venetsanopoulos said they include one of the largest oil and gas companies, for example.

And while you may not think of Athens as a startup hub, Venetsanopoulos argues that this is changing and there is a lot of talent there (though the company is also using the funding to build out its sales and marketing team in Silicon Valley). “There’s top-notch talent from top-notch universities that’s still untapped. It’s like we have an unfair advantage,” he said.

“We see a strong market opportunity as enterprises seek to leverage cloud-native solutions to unlock the benefits of machine learning,” Unusual’s Vrionis said. “Arrikto has taken an innovative and holistic approach to MLOps across the entire data, model and code lifecycle. Data scientists will be empowered to accelerate time to market through increased automation and collaboration without requiring engineering teams.”

Image Credits: Arrikto

#arrikto, #cloud, #cloud-computing, #cloud-infrastructure, #computing, #developer, #devops, #europe, #google, #john-vrionis, #kubeflow, #kubernetes, #machine-learning, #ml, #mlops, #recent-funding, #software-development, #startups, #unusual-ventures

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Dataloop raises $11M Series A round for its AI data management platform

Dataloop, a Tel Aviv-based startup that specializes in helping businesses manage the entire data lifecycle for their AI projects, including helping them annotate their datasets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed.

The Series A round was led by Amiti Ventures with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures.

“Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real time validation that can only be achieved with human input into the system,” said Dataloop CEO Eran Shlomo. “With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets.”

Image Credits: Dataloop

For the most part, Dataloop specializes in helping businesses manage and annotate their visual data. It’s agnostic to the vertical its customers are in, but we’re talking about anything from robotics and drones to retail and autonomous driving.

The platform itself centers around the ‘humans in the loop’ model that complements the automated systems with the ability for humans to train and correct the model as needed. It combines the hosted annotation platform with a Python SDK and REST API for developers, as well as a serverless Functions-as-a-Service environment that runs on top of a Kubernetes cluster for automating dataflows.

Image Credits: Dataloop

The company was founded in 2017. It’ll use the new funding to grow its presence in the U.S. and European markets, something that’s pretty standard for Israeli startups, and build out its engineering team as well.

#artificial-intelligence, #ceo, #enterprise, #free-software, #ml, #ourcrowd, #python, #serverless-computing, #tc, #tel-aviv, #united-states

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Grid AI raises $18.6M Series A to help AI researchers and engineers bring their models to production

Grid AI, a startup founded by the inventor of the popular open-source PyTorch Lightning project, William Falcon, that aims to help machine learning engineers more efficiently, today announced that it has raised an $18.6 million Series A funding round, which closed earlier this summer. The round was led by Index Ventures, with participation from Bain Capital Ventures and firstminute. 

Falcon co-founded the company with Luis Capelo, who was previously the head of machine learning at Glossier. Unsurprisingly, the idea here is to take PyTorch Lightning, which launched about a year ago, and turn that into the core of Grid’s service. The main idea behind Lightning is to decouple the data science from the engineering.

The time argues that a few years ago, when data scientists tried to get started with deep learning, they didn’t always have the right expertise and it was hard for them to get everything right.

“Now the industry has an unhealthy aversion to deep learning because of this,” Falcon noted. “Lightning and Grid embed all those tricks into the workflow so you no longer need to be a PhD in AI nor [have] the resources of the major AI companies to get these things to work. This makes the opportunity cost of putting a simple model against a sophisticated neural network a few hours’ worth of effort instead of the months it used to take. When you use Lightning and Grid it’s hard to make mistakes. It’s like if you take a bad photo with your phone but we are the phone and make that photo look super professional AND teach you how to get there on your own.”

As Falcon noted, Grid is meant to help data scientists and other ML professionals “scale to match the workloads required for enterprise use cases.” Lightning itself can get them partially there, but Grid is meant to provide all of the services its users need to scale up their models to solve real-world problems.

What exactly that looks like isn’t quite clear yet, though. “Imagine you can find any GitHub repository out there. You get a local copy on your laptop and without making any code changes you spin up 400 GPUs on AWS — all from your laptop using either a web app or command-line-interface. That’s the Lightning “magic” applied to training and building models at scale,” Falcon said. “It is what we are already known for and has proven to be such a successful paradigm shift that all the other frameworks like Keras or TensorFlow, and companies have taken notice and have started to modify what they do to try to match what we do.”

The service is now in private beta.

With this new funding, Grid, which currently has 25 employees, plans to expand its team and strengthen its corporate offering via both Grid AI and through the open-source project. Falcon tells me that he aims to build a diverse team, not in the least because he himself is an immigrant, born in Venezuela, and a U.S. military veteran.

“I have first-hand knowledge of the extent that unethical AI can have,” he said. “As a result, we have approached hiring our current 25 employees across many backgrounds and experiences. We might be the first AI company that is not all the same Silicon Valley prototype tech-bro.”

“Lightning’s open-source traction piqued my interest when I first learned about it a year ago,” Index Ventures’ Sarah Cannon told me. “So intrigued in fact I remember rushing into a closet in Helsinki while at a conference to have the privacy needed to hear exactly what Will and Luis had built. I promptly called my colleague Bryan Offutt who met Will and Luis in SF and was impressed by the ‘elegance’ of their code. We swiftly decided to participate in their seed round, days later. We feel very privileged to be part of Grid’s journey. After investing in seed, we spent a significant amount with the team, and the more time we spent with them the more conviction we developed. Less than a year later and pre-launch, we knew we wanted to lead their Series A.”

#artificial-intelligence, #bain-capital-ventures, #cloud, #deep-learning, #developer, #enterprise, #free-software, #github, #grid-ai, #helsinki, #index-ventures, #machine-learning, #ml, #neural-network, #pytorch, #recent-funding, #sarah-cannon, #startups, #tc, #torch, #united-states, #venezuela, #web-app, #william-falcon

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Arm CEO Simon Segars discusses AI, data centers, getting acquired by Nvidia and more

Nvidia is in the process of acquiring chip designer Arm for $40 billion. Coincidentally, both companies are also holding their respective developer conferences this week. After he finished his keynote at the Arm DevSummit, I sat down with Arm CEO Simon Segars to talk about the acquisition and what it means for the company.

Segars noted that the two companies started talking in earnest around May 2020, though at first, only a small group of executives was involved. Nvidia, he said, was really the first suitor to make a real play for the company — with the exception of SoftBank, of course, which took Arm private back in 2016 — and combining the two companies, he believes, simply makes a lot of sense at this point in time.

“They’ve had a meteoric rise. They’ve been building up to that,” Segars said. “So it just made a lot of sense with where they are at, where we are at and thinking about the future of AI and how it’s going to go everywhere and how that necessitates much more sophisticated hardware — and a much more sophisticated software environment on which developers can build products. The combination of the two makes a lot of sense in this moment.”

The data center market, where Nvidia, too, is already a major player, is also an area where Arm has heavily focused in recent years. And while it goes up against the likes of Intel, Segars is optimistic. “We’re not in it to be a bit player,” he said. “Our goal is to get a material market share and I think the proof to the pudding is there.”

He also expects that in a few years, we’ll see Arm-powered servers available on all of the major clouds. Right now, AWS is ahead in this game with its custom-built Gravitron processors. Microsoft and Google do not currently offer Arm-based servers.

“With each passing day, more and more of the software infrastructure that’s required for the cloud is getting ported over and optimized for Arm. So it becomes a more and more compelling proposition for sure,” he said, and cited both performance and energy efficiency as reasons for cloud providers to use Arm chips.

Another interesting aspect of the deal is that we may just see Arm sell some of Nvidia’s IP as well. That would be a big change — and a first — for Nvidia, but Segars believes it makes a lot of sense to do so.

“It may be that there is something in the portfolio of Nvidia that they currently sell as a chip that we may look at and go, ‘you know, what if we package that up as an IP product, without modifying it? There’s a market for that.’ Or it may be that there’s a thing in here where if we take that and combine it with something else that we were doing, we can make a better product or expand the market for the technology. I think it’s going to be more of the latter than it is the former because we design all our products to be delivered as IP.”

And while he acknowledged that Nvidia and Arm still face some regulatory hurdles, he believes the deal will be pro-competitive in the end — and that the regulators will see it the same way.

He does not believe, by the way, that the company will face any issues with Chinese companies not being able to license Arm’s designs because of export restrictions, something a lot of people were worried about when the deal was first announced.

“Export control of a product is all about where was it designed and who designed it,” he said. “And of course, just because your parent company changes, doesn’t change those fundamental properties of the underlying product. So we analyze all our products and look at how much U.S. content is in there, to what extent are our products subject to U.S. export control, U.K. export control, other export control regimes? It’s a full-time piece of work to make sure we stay on top of that.”

Here are some excerpts from our 30-minute conversation:

TechCrunch: Walk me through how that deal came about? What was the timeline for you?

Simon Segars: I think probably around May, June time was when it really kicked off. We started having some early discussions. And then, as these things progress, you suddenly kind of hit the ‘Okay, now let’s go.’ We signed a sort of first agreement to actually go into due diligence and then it really took off. It went from a few meetings, a bit of negotiation, to suddenly heads down and a broader set of people — but still a relatively small number of people involved, answering questions. We started doing due diligence documents, just the mountain of stuff that you go through and you end up with a document. [Segars shows a print-out of the contract, which is about the size of two phone books.]

You must have had suitors before this. What made you decide to go ahead with this deal this time around?

Well, to be honest, in Arm’s history, there’s been a lot of rumors about people wanting to acquire Arm, but really until SoftBank in 2016, nobody ever got serious. I can’t think of a case where somebody actually said, ‘come on, we want to try and negotiate a deal here.’ And so it’s been four years under SoftBank’s ownership and that’s been really good because we’ve been able to do what we said we were going to do around investing much more aggressively in the technology. We’ve had a relationship with Nvidia for a long time. [Rene Haas, Arm’s president of its Intellectual Property Group, who previously worked at Nvidia] has had a relationship with [Nvidia CEO Jensen Huang] for a long time. They’ve had a meteoric rise. They’ve been building up to that. So it just made a lot of sense with where they are at, where we are at and thinking about the future of AI and how it’s going to go everywhere and how that necessitates much more sophisticated hardware — and a much more sophisticated software environment on which developers can build products. The combination of the two makes a lot of sense in this moment.

How does it change the trajectory you were on before for Arm?

#arm-holdings, #artificial-intelligence, #autonomous-systems, #aws, #china, #cloud, #energy-efficiency, #google, #hardware, #intel, #jensen-huang, #ma, #machine-learning, #microsoft, #ml, #nvidia, #simon-segars, #softbank, #tc

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Deep Science: Robot perception, acoustic monitoring, using ML to detect arthritis

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.

The topics in this week’s Deep Science column are a real grab bag that range from planetary science to whale tracking. There are also some interesting insights from tracking how social media is used and some work that attempts to shift computer vision systems closer to human perception (good luck with that).

ML model detects arthritis early

Image Credits: UC San Diego

One of machine learning’s most reliable use cases is training a model on a target pattern, say a particular shape or radio signal, and setting it loose on a huge body of noisy data to find possible hits that humans might struggle to perceive. This has proven useful in the medical field, where early indications of serious conditions can be spotted with enough confidence to recommend further testing.

This arthritis detection model looks at X-rays, same as doctors who do that kind of work. But by the time it’s visible to human perception, the damage is already done. A long-running project tracking thousands of people for seven years made for a great training set, making the nearly imperceptible early signs of osteoarthritis visible to the AI model, which predicted it with 78% accuracy three years out.

The bad news is that knowing early doesn’t necessarily mean it can be avoided, as there’s no effective treatment. But that knowledge can be put to other uses — for example, much more effective testing of potential treatments. “Instead of recruiting 10,000 people and following them for 10 years, we can just enroll 50 people who we know are going to be getting osteoarthritis … Then we can give them the experimental drug and see whether it stops the disease from developing,” said co-author Kenneth Urish. The study appeared in PNAS.

Using acoustic monitoring to preemptively save the whales

It’s amazing to think that ships still collide with and kill large whales on a regular basis, but it’s true. Voluntary speed reductions haven’t been much help, but a smart, multisource system called Whale Safe is being put in play in the Santa Barbara channel that could hopefully give everyone a better idea of where the creatures are in real-time.

Image Credits: UW/UC Santa Barbara

The system uses underwater acoustic monitoring, near-real-time forecasting of likely feeding areas, actual sightings and a dash of machine learning (to identify whale calls quickly) to produce a prediction for whale presence along a given course. Large container ships can then make small adjustments well-ahead of time instead of trying to avoid a pod at the last minute.

“Predictive models like this give us a clue for what lies ahead, much like a daily weather forecast,” said Briana Abrahms, who led the effort from the University of Washington. “We’re harnessing the best and most current data to understand what habitats whales use in the ocean, and therefore where whales are most likely to be as their habitats shift on a daily basis.”

Incidentally, Salesforce founder Marc Benioff and his wife Lynne helped establish the UC Santa Barbara center that made this possible.

#artificial-intelligence, #cognitive-science, #cybernetics, #deep-science, #epfl, #lab-wrap, #machine-learning, #ml, #science, #simulation, #social, #tc

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WhyLabs brings more transparancy to ML ops

WhyLabs, a new machine learning startup that was spun out of the Allen Institute, is coming out of stealth today. Founded by a group of former Amazon machine learning engineers, Alessya Visnjic, Sam Gracie and Andy Dang, together with Madrona Venture Group principal Maria Karaivanova, WhyLabs’ focus is on ML operations after models have been trained — not on building those models from the ground up.

The team also today announced that it has raised a $4 million seed funding round from Madrona Venture Group, Bezos Expeditions, Defy Partners and Ascend VC.

Visnjic, the company’s CEO, used to work on Amazon’s demand forecasting model.

“The team was all research scientists, and I was the only engineer who had kind of tier-one operating experience,” she told me. “So it was like, ”Okay, how bad could it be?’ I carried the pager for the retail website before it can be bad. But it was one of the first AI deployments that we’d done at Amazon at scale. The pager duty was extra fun because there were no real tools. So when things would go wrong — like we’d order way too many black socks out of the blue — it was a lot of manual effort to figure out why was this happening.”

Image Credits: WhyLabs

But while large companies like Amazon have built their own internal tools to help their data scientists and AI practitioners operate their AI systems, most enterprises continue to struggle with this — and a lot of AI projects simply fail and never make it into production. “We believe that one of the big reasons that happens is because of the operating process that remains super manual,” Visnjic said. “So at WhyLabs, we’re building the tools to address that — specifically to monitor and track data quality and alert — you can think of it as Datadog for AI applications.”

The team has brought ambitions, but to get started, it is focusing on observability. The team is building — and open-sourcing — a new tool for continuously logging what’s happening in the AI system, using a low-overhead agent. That platform-agnostic system, dubbed WhyLogs, is meant to help practitioners understand the data that moves through the AI/ML pipeline.

For a lot of businesses, Visnjic noted, the amount of data that flows through these systems is so large that it doesn’t make sense for them to keep “lots of big haystacks with possibly some needles in there for some investigation to come in the future.” So what they do instead is just discard all of this. With its data logging solution, WhyLabs aims to give these companies the tools to investigate their data and find issues right at the start of the pipeline.

Image Credits: WhyLabs

According to Karaivanova, the company doesn’t have paying customers yet, but it is working on a number of proofs of concepts. Among those users is Zulily, which is also a design partner for the company. The company is going after mid-size enterprises for the time being, but as Karaivanova noted, to hit the sweet spot for the company, a customer needs to have an established data science team with 10 to 15 ML practitioners. While the team is still figuring out its pricing model, it’ll likely be a volume-based approach, Karaivanova said.

“We love to invest in great founding teams who have built solutions at scale inside cutting-edge companies, who can then bring products to the broader market at the right time. The WhyLabs team are practitioners building for practitioners. They have intimate, first-hand knowledge of the challenges facing AI builders from their years at Amazon and are putting that experience and insight to work for their customers,” said Tim Porter, managing director at Madrona. “We couldn’t be more excited to invest in WhyLabs and partner with them to bring cross-platform model reliability and observability to this exploding category of MLOps.”

#amazon, #artificial-general-intelligence, #artificial-intelligence, #bezos-expeditions, #cybernetics, #datadog, #defy-partners, #engineer, #enterprise, #jeff-bezos, #machine-learning, #madrona-venture-group, #ml, #mlops, #performance-management, #recent-funding, #science-and-technology, #startups, #tc, #whylabs

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Join us Wednesday, September 9 to watch Techstars Starburst Space Accelerator Demo Day live

The 2020 class of Techstars’ Starburst Space Accelerator are graduating with an official Demo Day on Wednesday at 10 AM PT (1 PM ET), and you can watch all the teams present their startups live via the stream above. This year’s class includes 10 companies building innovative new solutions to challenges either directly or indirectly related to commercial space.

Techstars Starburst is a program with a lot of heavyweight backing from both private industry and public agencies, including from NASA’s JPL, the U.S. Air Force, Lockheed Martin, Maxar Technologies, SAIC, Israel Aerospace Industries North America, and The Aerospace Corporation. The program, led by Managing Director Matt Kozlov, is usually based locally in LA, where much of the space industry has significant presence, but this year the Demo Day is going online due to the ongoing COVID-19 situation.

Few, if any, programs out there can claim such a broad representation of big-name partners from across commercial, military and general civil space in terms of stakeholders, which is the main reason it manages to attract a range of interesting startups.  This is the second class of graduating startups from the Starburst Space Accelerator; last year’s batch included some exceptional standouts like on-orbit refuelling company Orbit Fab (also a TechCrunch Battlefield participant), imaging micro-satellite company Pixxel and satellite propulsion company Morpheus.

As for this year’s class, you can check out a full list of all ten participating companies below. The demo day presentations begin tomorrow, September 9 at 10 AM PT/1 PM PT, so you can check back in here then to watch live as they provide more details about what it is they do.

Bifrost

A synthetic data API that allows AI teams to generate their own custom datasets up to 99% faster – no tedious collection, curation or labelling required.
founders@bifrost.ai

Holos Inc.

A virtual reality content management system that makes it super easy for curriculum designers to create and deploy immersive learning experiences.
founders@holos.io

Infinite Composites Technologies

The most efficient gas storage systems in the universe.
founders@infinitecomposites.com

Lux Semiconductors

Lux is developing next generation System-on-Foil electronics.
founders@luxsemiconductors.com

Natural Intelligence Systems, Inc.

Developer of next generation pattern based AI/ML systems.
leadership@naturalintelligence.ai

Prewitt Ridge

Engineering collaboration software for teams building challenging deep tech projects.
founders@prewittridge.com

SATIM

Providing satellite radar-based intelligence for decision makers.
founders@satim.pl

Urban Sky

Developing stratospheric Microballoons to capture the freshest, high-res earth observation data.
founders@urbansky.space

vRotors

Real-time remote robotic controls.
founders@vrotors.com

WeavAir

Proactive air insights.
founders@weavair.com

#aerospace, #artificial-intelligence, #astronomy, #collaboration-software, #content-management-system, #demo-day, #electronics, #imaging, #israel-aerospace-industries, #lockheed-martin, #louisiana, #matt-kozlov, #maxar-technologies, #ml, #orbit-fab, #outer-space, #robotics, #saic, #satellite, #science, #space, #spaceflight, #startups, #tc, #techstars, #u-s-air-force, #virtual-reality

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How to diagnose and treat machine learning models afflicted by COVID-19

COVID-19 has disrupted the lives of millions of people and affected businesses across the world. Its impact has been particularly significant on many machine learning (ML) models that companies use to predict human behavior.

Companies need to take steps to deeply examine ML models and acquire the insights needed to effectively update models and surrounding business rules.

The economic disruption of COVID-19 has been unprecedented in its swiftness, upsetting supply lines, temporarily closing retail stores and changing online customer behaviors. It has also dramatically increased unemployment overnight, increasing financial stress and systemic risks of both individuals and businesses. It is forecasted that global GDP could be affected by up to 0.9%, on a par with the 2008 financial crisis. While the nature of our recovery is unknown, if the 2008 crisis is any indicator, the impact of COVID-19 could be felt for years, through both short-term adjustments and long-term shifts in consumer and business behaviors and attitudes.

This disruption impacts machine learning models because the concepts and relationships the models learned when they were trained may no longer hold. This phenomenon is called “concept drift.” ML models may become unstable and underperform in the face of concept drift. That is precisely what is happening now with COVID-19. The effects of these drifts will be felt for quite some time, and models will need to be adjusted to keep up. The good news is that there have been significant developments in model intelligence technology, and through judicious use, models can nimbly adjust to those drifts.

As the effects of COVID-19 (and economic closure and reopening) play out, there will be distinct stages in the impact on social and economic behaviors. Updates to business rules and models will need to be done in sync with overall behavior shifts in each of these stages. Companies need to adopt an approach of measure-understand-act and to constantly examine, assess and adjust ML models in production or development and surrounding business rules.

Examining how ML models have been impacted means going through an exercise to both measure and understand how the models behaved prior to the coronavirus, how they are behaving now, why they are behaving differently (i.e., what inputs and relationships are the drivers of change), and then to determine if the new behavior is expected and accurate, or is no longer valid. Once this is determined, the next step is naturally to act: “So, what can we do about it?”

#artificial-intelligence, #bank, #banking, #column, #coronavirus, #covid-19, #cryptocurrency, #developer, #ecommerce, #extra-crunch, #finance, #fintech, #machine-learning, #market-analysis, #ml, #predictive-analytics, #product-development, #product-search, #retail-stores, #search-results, #startups, #tc, #work

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In conversation with European B2B seed VC La Famiglia

Earlier this month, La Famiglia, a Berlin-based VC firm that invests in seed-stage European B2B tech startups, disclosed that it raised a second fund totaling €50 million, up from its debut fund of €35 million in 2017.

The firm writes first checks of up to €1.5 million in European startups that use technology to address a significant need within an industry. It’s backed 37 startups to date (including Forto, Arculus and Graphy) and seeks to position itself based on its industry network, many of whom are LPs.

La Famiglia’s investors include the Mittal, Pictet, Oetker, Hymer and Swarovski families, industry leaders Voith and Franke, as well as the families behind conglomerates such as Hapag-Lloyd, Solvay, Adidas and Valentino. In addition, the likes of Niklas Zennström (Skype, Atomico), Zoopla’s Alex Chesterman and Personio’s Hanno Renner are also LPs.

Meanwhile, the firm describes itself as “female-led,” with founding partner Dr. Jeannette zu Fürstenberg and partner Judith Dada at the helm.

With the ink only just dry on the new fund, I put questions to the pair to get more detail on La Famiglia’s investment thesis and what it looks for in founders. We also discussed how the firm taps its “old economy” network, the future of industry 4.0 and what La Famiglia is doing — if anything — to ensure it backs diverse founders.

TechCrunch: You describe La Famiglia as B2B-focused, writing first checks of up to €1.5 million in European startups using technology to address a significant need within an industry. In particular, you cite verticals such as logistics and supply chain, the industrial space, and insurance, while also referencing sustainability and the future of work.

Can you elaborate a bit more on the fund’s remit and what you look for in founders and startups at such an early stage?

Jeannette zu Fürstenberg: Our ambition is to capture the fundamental shift in value creation across the largest sectors of our European economy, which are either being disrupted or enabled by digital technologies. We believe that opportunities in fields such as manufacturing or logistics will be shaped by a deep process understanding of these industries, which is the key differentiator in creating successful outcomes and a strength that European entrepreneurs can leverage.

We look for visionary founders who see a new future, where others only see fragments, with grit to push through adversity and a creative force to shape the world into being.

Judith Dada: Picking up a lot of signals from various expert sources in our network informs the opportunity landscape we see and allows us to invest with a strong sense of market timing. Next to verticals like insurance or industrial manufacturing, we also invest into companies tackling more horizontal opportunities, such as sustainability in its vast importance across industries, as well as new ways that our work is being transformed, for workers of all types. We look for opportunities across a spectrum of technological trends, but are particularly focused on the application potential of ML and AI.

#adidas, #artificial-intelligence, #berlin, #coinbase, #entrepreneurship, #europe, #extra-crunch, #facebook, #finance, #hanno-renner, #judith-dada, #la-famiglia, #logistics, #machine-learning, #market-analysis, #ml, #niklas-zennstrom, #private-equity, #solvay, #supply-chain, #swarovski, #tc, #valentino

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Here are a few ways GPT-3 can go wrong

OpenAI’s latest language generation model, GPT-3, has made quite the splash within AI circles, astounding reporters to the point where even Sam Altman, OpenAI’s leader, mentioned on Twitter that it may be overhyped. Still, there is no doubt that GPT-3 is powerful. Those with early-stage access to OpenAI’s GPT-3 API have shown how to translate natural language into code for websites, solve complex medical question-and-answer problems, create basic tabular financial reports, and even write code to train machine learning models — all with just a few well-crafted examples as input (i.e., via “few-shot learning”).

Soon, anyone will be able to purchase GPT-3’s generative power to make use of the language model, opening doors to build tools that will quietly (but significantly) shape our world. Enterprises aiming to take advantage of GPT-3, and the increasingly powerful iterations that will surely follow, must take great care to ensure that they install extensive guardrails when using the model, because of the many ways that it can expose a company to