Jul
11
2019
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Andrew Ng to talk about how AI will transform business at TC Sessions: Enterprise

When it comes to applying AI to the world around us, Andrew Ng has few if any peers. We are delighted to announce that the renowned founder, investor, AI expert and Stanford professor will join us onstage at the TechCrunch Sessions: Enterprise show on September 5 at the Yerba Buena Center in San Francisco. 

AI promises to transform the $500 billion enterprise world like nothing since the cloud and SaaS. Hundreds of startups are already seizing the AI moment in areas like recruiting, marketing and communications and customer experience. The oceans of data required to power AI are becoming dramatically more valuable, which in turn is fueling the rise of new data platforms, another big topic of the show

Last year, Ng launched the $175 million AI Fund, backed by big names like Sequoia, NEA, Greylock and SoftBank. The fund’s goal is to develop new AI businesses in a studio model and spin them out when they are ready for prime time. The first of that fund’s cohort is Landing AI, which also launched last year and aims to “empower companies to jumpstart AI and realize practical value.” It’s a wave businesses will want to catch if Ng is anywhere near right in his conviction that AI will generate $13 trillion in GDP growth globally in the next 20 years. You heard that right. 

At TC Sessions: Enterprise, TechCrunch’s editors will ask Ng to detail how he believes AI will unfold in the enterprise world and bring big productivity gains to business. 

As the former chief scientist at Baidu and the founding lead of Google Brain, Ng led the AI transformation of two of the world’s leading technology companies. Dr. Ng is the co-founder of Coursera, an online learning platform, and founder of deeplearning.ai, an AI education platform. Dr. Ng is also an adjunct professor at Stanford University’s Computer Science Department and holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

Early Bird tickets to see Andrew at TC Sessions: Enterprise are on sale for just $249 when you book here; but hurry, prices go up by $100 soon! Students, grab your discounted tickets for just $75 here.


Feb
05
2019
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Backed by Benchmark, Blue Hexagon just raised $31 million for its deep learning cybersecurity software

Nayeem Islam spent nearly 11 years with chipmaker Qualcomm, where he founded its Silicon Valley-based R&D facility, recruited its entire team and oversaw research on all aspects of security, including applying machine learning on mobile devices and in the network to detect threats early.

Islam was nothing if not prolific, developing a system for on-device machine learning for malware detection, libraries for optimizing deep learning algorithms on mobile devices and systems for parallel compute on mobile devices, among other things.

In fact, because of his work, he also saw a big opportunity in better protecting enterprises from cyberthreats through deep neural networks that are capable of processing every raw byte within a file and that can uncover complex relations within data sets. So two years ago, Islam and Saumitra Das, a former Qualcomm engineer with 330 patents to his name and another 450 pending, struck out on their own to create Blue Hexagon, a now 30-person Sunnyvale, Calif.-based company that is today disclosing it has raised $31 million in funding from Benchmark and Altimeter.

The funding comes roughly one year after Benchmark quietly led a $6 million Series A round for the firm.

So what has investors so bullish on the company’s prospects, aside from its credentialed founders? In a word, speed, seemingly. According to Islam, Blue Hexagon has created a real-time, cybersecurity platform that he says can detect known and unknown threats at first encounter, then block them in “sub seconds” so the malware doesn’t have time to spread.

The industry has to move to real-time detection, he says, explaining that four new and unique malware samples are released every second, and arguing that traditional security methods can’t keep pace. He says that sandboxes, for example, meaning restricted environments that quarantine cyberthreats and keep them from breaching sensitive files, are no longer state of the art. The same is true of signatures, which are mathematical techniques used to validate the authenticity and integrity of a message, software or digital document but are being bypassed by rapidly evolving new malware.

Only time will tell if Blue Hexagon is far more capable of identifying and stopping attackers, as Islam insists is the case. It is not the only startup to apply deep learning to cybersecurity, though it’s certainly one of the first. Critics, some who are protecting their own corporate interests, also worry that hackers can foil security algorithms by targeting the warning flags they look for.

Still, with its technology, its team and its pitch, Blue Hexagon is starting to persuade not only top investors of its merits, but a growing — and broad — base of customers, says Islam. “Everyone has this issue, from large banks, insurance companies, state and local governments. Nowhere do you find someone who doesn’t need to be protected.”

Blue Hexagon can even help customers that are already under attack, Islam says, even if it isn’t ideal. “Our goal is to catch an attack as early in the kill chain as possible. But if someone is already being attacked, we’ll see that activity and pinpoint it and be able to turn it off.”

Some damage may already be done, of course. It’s another reason to plan ahead, he says. “With automated attacks, you need automated techniques.” Deep learning, he insists, “is one way of leveling the playing field against attackers.”

Apr
25
2018
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Allegro.AI nabs $11M for ‘deep learning as a service’, for businesses to build computer vision products

Artificial intelligence and the application of it across nearly every aspect of our lives is shaping up to be one of the major step changes of our modern society. Today, a startup that wants to help other companies capitalise on AI’s advances is announcing funding and emerging from stealth mode.

Allegro.AI, which has built a deep learning platform that companies can use to build and train computer-vision-based technologies — from self-driving car systems through to security, medical and any other services that require a system to read and parse visual data — is today announcing that it has raised $11 million in funding, as it prepares for a full-scale launch of its commercial services later this year after running pilots and working with early users in a closed beta.

The round may not be huge by today’s startup standards, but the presence of strategic investors speaks to the interest that the startup has sparked and the gap in the market for what it is offering. It includes MizMaa Ventures — a Chinese fund that is focused on investing in Israeli startups, along with participation from Robert Bosch Venture Capital GmbH (RBVC), Samsung Catalyst Fund and Israeli fund Dynamic Loop Capital. Other investors (the $11 million actually covers more than one round) are not being disclosed.

Nir Bar-Lev, the CEO and cofounder (Moses Guttmann, another cofounder, is the company’s CTO; and the third cofounder, Gil Westrich, is the VP of R&D), started Allegro.AI first as Seematics in 2016 after he left Google, where he had worked in various senior roles for over 10 years. It was partly that experience that led him to the idea that with the rise of AI, there would be an opportunity for companies that could build a platform to help other less AI-savvy companies build AI-based products.

“We’re addressing a gap in the industry,” he said in an interview. Although there are a number of services, for example Rekognition from Amazon’s AWS, which allow a developer to ping a database by way of an API to provide analytics and some identification of a video or image, these are relatively basic and couldn’t be used to build and “teach” full-scale navigation systems, for example.

“An ecosystem doesn’t exist for anything deep-learning based.” Every company that wants to build something would have to invest 80-90 percent of their total R&D resources on infrastructure, before getting to the many other apsects of building a product, he said, which might also include the hardware and applications themselves. “We’re providing this so that the companies don’t need to build it.”

Instead, the research scientists that will buy in the Allegro.AI platform — it’s not intended for non-technical users (not now at least) — can concentrate on overseeing projects and considering strategic applications and other aspects of the projects. He says that currently, its direct target customers are tech companies and others that rely heavily on tech, “but are not the Googles and Amazons of the world.”

Indeed, companies like Google, AWS, Microsoft, Apple and Facebook have all made major inroads into AI, and in one way or another each has a strong interest in enterprise services and may already be hosting a lot of data in their clouds. But Bar-Lev believes that companies ultimately will be wary to work with them on large-scale AI projects:

“A lot of the data that’s already on their cloud is data from before the AI revolution, before companies realized that the asset today is data,” he said. “If it’s there, it’s there and a lot of it is transactional and relational data.

“But what’s not there is all the signal-based data, all of the data coming from computer vision. That is not on these clouds. We haven’t spoken to a single automotive who is sharing that with these cloud providers. They are not even sharing it with their OEMs. I’ve worked at Google, and I know how companies are afraid of them. These companies are terrified of tech companies like Amazon and so on eating them up, so if they can now stop and control their assets they will do that.”

Customers have the option of working with Allegro either as a cloud or on-premise product, or a combination of the two, and this brings up the third reason that Allegro believes it has a strong opportunity. The quantity of data that is collected for image-based neural networks is massive, and in some regards it’s not practical to rely on cloud systems to process that. Allegro’s emphasis is on building computing at the edge to work with the data more efficiently, which is one of the reasons investors were also interested.

“AI and machine learning will transform the way we interact with all the devices in our lives, by enabling them to process what they’re seeing in real time,” said David Goldschmidt, VP and MD at Samsung Catalyst Fund, in a statement. “By advancing deep learning at the edge, Allegro.AI will help companies in a diverse range of fields—from robotics to mobility—develop devices that are more intelligent, robust, and responsive to their environment. We’re particularly excited about this investment because, like Samsung, Allegro.AI is committed not just to developing this foundational technology, but also to building the open, collaborative ecosystem that is necessary to bring it to consumers in a meaningful way.”

Allegro.AI is not the first company with hopes of providing AI and deep learning as a service to the enterprise world: Element.AI out of Canada is another startup that is being built on the premise that most companies know they will need to consider how to use AI in their businesses, but lack the in-house expertise or budget (or both) to do that. Until the wider field matures and AI know-how becomes something anyone can buy off-the-shelf, it’s going to present an interesting opportunity for the likes of Allegro and others to step in.

 

 

 

Mar
26
2018
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The Linux Foundation launches a deep learning foundation

Despite its name, the Linux Foundation has long been about more than just Linux. These days, it’s a foundation that provides support to other open source foundations and projects like Cloud Foundry, the Automotive Grade Linux initiative and the Cloud Native Computing Foundation. Today, the Linux Foundation is adding yet another foundation to its stable: the LF Deep Learning Foundation.

The idea behind the LF Deep Learning Foundation is to “support and sustain open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.”

The founding members of the new foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa and ZTE. Others will likely join in the future.

“We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems,” said Jim Zemlin, executive director of The Linux Foundation.

The foundation’s first official project is the Acumos AI Project, a collaboration between AT&T and Tech Mahindra that was already hosted by the Linux Foundation. Acumos AI is a platform for developing, discovering and sharing AI models and workflows.

Like similar Linux Foundation-based organizations, the LF Deep Learning Foundation will offer different membership levels for companies that want to support the project, as well as a membership level for non-profits. All LF Deep Learning members have to be Linux Foundation members, too.

Dec
14
2017
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Andrew Ng’s Landing.ai wants to bring artificial intelligence to the manufacturing industry, starting with Foxconn

 AI pioneer Andrew Ng is probably best known for his work on the Google Brain project and for leading Baidu’s AI group. After leaving Baidu earlier this year, it wasn’t quite clear what exactly Ng was up to, but today he announced the launch of Landing.ai, a new startup that focuses on bringing artificial intelligence to the manufacturing industry. Read More

Aug
07
2017
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IBM touts improved distributed training time for visual recognition models

 Two months ago, Facebook’s AI Research Lab (FAIR) published some impressive training times for massively distributed visual recognition models. Today IBM is firing back with some numbers of its own. IBM’s research groups says it was able to train ResNet-50 for 1k classes in 50 minutes across 256 GPUs — which is just the polite way of saying “my model trains faster than… Read More

Aug
03
2017
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Facebook finishes its move to neural machine translation

 Facebook announced this morning that it had completed its move to neural machine translation — a complicated way of saying that Facebook is now using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to automatically translate content across Facebook. Read More

Jun
08
2017
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Facebook is speeding up training for visual recognition models

 Every minute spent training a deep learning model is a minute not doing something else and in today’s fast paced world of research, that minute is worth a lot. Facebook published a paper this morning detailing its personal approach to this problem. The company says its managed to reduce the training time of a ResNet-50 deep learning model on ImageNet from 29 hours to one. Facebook managed… Read More

May
17
2017
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Google is giving a cluster of 1,000 Cloud TPUs to researchers for free

 At the end of Google I/O, the company unveiled a new program to give researchers access to the company’s most advanced machine learning technologies for free. The TensorFlow Research Cloud program, as it will be called, will be application based and open to anyone conducting research, rather than just members of academia. If accepted, researchers will get access to a cluster of 1,000… Read More

May
15
2017
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Riminder uses deep learning to better match people to jobs

 There’s nothing efficient about sorting through 30,000 resumes by hand. Recruiters spend months evaluating applicants only to have great prospective candidates get lost in the pile. At TechCrunch’s Startup Battlefield, French startup Riminder made the case for how its deep learning-powered platform could augment recruiters — helping them better surface ideal contenders for… Read More

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