Connect with us

Investments

Investments by Tech Giants In Artificial Intelligence is Set to Grow Further

mm

Published

 on

Investments by Tech Giants In Artificial Intelligence is Set to Grow Further

Investment figures into artificial intelligence are growing exponentially each year. According to the market researchers at Markets and Markets, the current estimate is that the AI market will reach $191 billion by the year 2025. The investment number for 2018 was $21.5 billion.

Taking a look at the phenomenon, British specialist publication TechWorld took a look at how 12 of the world’s technological giants are investing in the development of artificial intelligence. Here we present the current six leaders in that field.

Nvidia – One of the largest chipmakers is at the same time one of the most serious investors into AI technology, as chips are key to pushing the technology forward.  As noted, “AI requires more computational power than traditional algorithms, so heavy investment in new chip designs is an industry imperative.” So far, Nvidia has concentrated its AI investments in the technology’s use in the automotive industry, robotics, as well as the construction industry and healthcare.

Nvidia’s partnership with Nuance uses deep learning chips and a platform to bring AI to medical imaging. “The AI-powered imaging service enables medical professionals to use new tools to analyze x-rays and other needs in radiology.”

Intel – They recently invested $117 million in 14 startups that are developing AI platforms. In 2016 Intel also acquired Nervana and Movidius, which led to the lunch of Nervana Neural Network Processor (NNP) in 2019. This is processor is specially designed “to accelerate the training and inferences from AI models.”

In a blog post,  VP and GM of the artificial intelligence products group at Intel said the following:

“In an AI-empowered world, we will need to adapt hardware solutions into a combination of processors tailored to specific use cases – like inference processing at the edge – and customer needs that best deliver breakthrough insights.”

Google’s CEO Sundar Pichai, explained in 2016 that “In an AI-empowered world, we will need to adapt hardware solutions into a combination of processors tailored to specific use cases – like inference processing at the edge – and customer needs that best deliver breakthrough insights.”

DeepMind, AI startup which Google bought in 2014 for $400 million has been used for, among other things,

to find the quickest route between underground stations,

 defeat champion players of the board game ‘Go’, and to improve healthcare through a series of controversial agreements with the NHS.”

At the same time, AI has a key role in a number of Google’s products like Google Mail or Google Assistant.

Amazon’s use of AI ranges from its online recommendations engine to using robots in its warehouses. Its most developed use of consumer-oriented AI lies with Alexa “the company Amazon’s intelligent voice assistant, uses neural networks to power natural language processing that analyses the human voice and returns an appropriate response.”

Amazon has also brought machine learning to AWS. Customers can use Lex to build conversational interfaces, Polly to turn text into speech, Rekognition to add images analysis to applications, Comprehend to find insights and relationships in text, or SageMaker to build, train, and deploy machine learning models at scale.”

Microsoft’s large-scale investments into AI went a step forward when the company created Artificial Intelligence and Research Group that encompasses the Windows, Office, and Azure business units.

Along with it, in 2016 Microsoft launched Microsoft Ventures, a fund specifically devoted to AI startups. Since then, the company’s AI investments grew constantly, when only in 2018 Microsoft bought five Ai tech companies.

This year the company announced plans to “invest $1 billion in OpenAI, a San Francisco-based company. It also announced a two-year partnership to develop AI supercomputing technologies on Microsoft Azure.”

Apple seems to be trying to catch up with its competitors as far as AI technology is concerned as it “recently hired Google’s chief of search and artificial intelligence, John Giannandrea, to run its machine learning and AI strategy.”

The company has also started investing in a wider range of AI startups, like Emotient, which produces facial recognition technology that can detect how customers reaction to ads, Vocal IQ, which provides a platform for voice interfaces, and Silk Labs, which makes AI software that can fit in consumer devices.

Apple has also come up with an academic paper that focuses on the use of AI in protecting user data and providing transparency. 

The rest of the list is available here.

Spread the love

Deep Learning Specialization on Coursera

Former diplomat and translator for the UN, currently freelance journalist/writer/researcher, focusing on modern technology, artificial intelligence, and modern culture.

Investments

Microsoft Partners with Startup Graphcore to Develop AI Chips

mm

Published

on

Microsoft Partners with Startup Graphcore to Develop AI Chips

Microsoft hopes that its Azure cloud platform will catch up in popularity with Amazon and Google, so as Wired reports, it has partnered with a British startup Graphcore to come up with a new computer chip that would be able to sustain all-new artificial intelligence developments.

As Wired notes, Bristo, UK startup Graphcore “has attracted considerable attention among AI researchers—and several hundred million dollars in investment—on the promise that its chips will accelerate the computations required to make AI work.” Since its inception in 2016, this is the first time that the company is publicly coming up with its chips and testing results.

Microsoft’s invested in Graphcore in December 2018 “as a part of a $200 million funding round”, as it wants to stimulate the use of its cloud services to a growing number of customers that use AI applications.

Graphcore itself designed its chips from scratch “to support the calculations that help machines to recognize facesunderstand speechparse languagedrive cars, and train robots.” The company expects that its chips will be used by “companies running business-critical operations on AI, such as self-driving car startups, trading firms, and operations that process large quantities of video and audio, as well as those working on next-generation AI algorithms.”

According to the benchmarks published by Microsoft and Graphcore on November 13, 2019, “the chip matches or exceeds the performance of the top AI chips from Nvidia and Google using algorithms written for those rival platforms. Code is written specifically for Graphcore’s hardware maybe even more efficient.”

The two companies also stated that “certain image-processing tasks work many times faster on Graphcore’s chips,” and that “ they were able to train a popular AI model for language processing, called BERT, at rates matching those of any other existing hardware.”

Moor Insights AI chip specialist Karl Freund is of the opinion that the results of the new chip show that it is “cutting-edge but still flexible,”  and that “they’ve done a good job making it programmable,” an extremely hard thing to do.

Wired further adds that Nigel Toon, co-founder, and CEO of Graphcore, says the companies began working together a year after his company’s launch, through Microsoft Research Cambridge in the UK. He also told the publication that his company’s chips are especially well-suited to tasks that involve very large AI models or temporal data. Also, one customer in finance supposedly saw a 26-fold performance boost in an algorithm used to analyze market data thanks to Graphcore’s hardware.

Some other, smaller companies used this occasion to announce that “they are working with Graphcore chips through Azure.” This includes Citadel, which will use the chips to analyze financial data, and Qwant, a European search engine that wants the hardware to run an image-recognition algorithm known as ResNext.

Spread the love

Deep Learning Specialization on Coursera
Continue Reading

Investments

Senators Began To Get Involved In AI

Published

on

Senators Began To Get Involved In AI

According to the top Democrat in the U.S. Senate, Senator Chuck Schumer (D-NY), the U.S. government should make a massive investment in artificial intelligence. He is advocating for the government to create a brand new agency to invest $100 billion in basic research in AI over 5 years. According to the senator, this will help the United States compete against Russia and China, who are moving ahead quickly in the field. The agency will also provide funding to certain areas where U.S. companies are not heavily involved. 

Senator Schumer gave a speech last week to senior national security and research policy-makers who gathered in Washington D.C. It was the first time he publicly outlined the new plan, and he is in an influential position to make progress as minority leader. This comes at a time when there is an increasing level of interest in AI and other related fields including robotics. There has also been a recent presidential executive order.

The new national science tech fund would invest $100 billion into “fundamental research related to AI and some other cutting-edge areas.”

Some of those cutting edge areas include quantum computing, 5G networks, robotics, cybersecurity, and biotechnology. The money would be used to fund research at U.S. universities, companies, and other federal agencies. It would also fund “testbed facilities” used to complete work needed to turn discoveries into commercial products. 

Behind Closed Doors

This plan has been discussed behind closed doors for several months by tech industry executives and academic leaders, but it still has a long way to go. According to Schumer, “this is just a discussion draft.”

Schumer suggested the fund would be a “subsidiary” of the National Science Foundation (NSF). It would also have a connection to the Defense Advanced Research Projects Agency (DARPA) within the Department of Defense (DOD) and have a board of directors. 

National Security Commission on Artificial Intelligence

The speech took place at a symposium sponsored by the National Security Commission on Artificial Intelligence, which is a bipartisan body that was created by Congress. This issue can bring together politicians from both parties, especially during a time when the government is so divided over the impeachment proceedings taking place against President Donald Trump. 

“This should not be a partisan issue. This is about the future of America,” Schumer asserted, saying the country’s security and economic prosperity depend on making such a major investment. And he asked the politically well-connected audience to help him sell the proposal.

“This idea has support from some people very close to the president and very close to [Senate Majority Leader] Mitch McConnell [R],” Schumer said. “But thus far they have been unable to get their [principals’] full-throated support. Anyone here who has any relationship with those people or people near them should be pushing this.”

The U.S. Government 

The U.S. government has not been completely absent from artificial intelligence, but many believe more needs to be done to keep pace with technology which will revolutionize almost everything.

Last month, the Department of Energy released plans to request $3 billion to $4 billion from Congress over the next 10 years. It will be used for AI research which already has some investment taking place. NSF officials have said that the agency spends that amount each year over the past decade in order to improve AI algorithms and software. 

Trump issued an executive order in February that told NSF, DOD, and other federal agencies to invest more in high-performance computing. Under the order, federal agencies are required to develop an “action plan to protect the U.S. advantage in AI technology.”

 

Spread the love

Deep Learning Specialization on Coursera
Continue Reading

Healthcare

London-Based Startup LabGenius Raises $10M

Published

on

London-Based Startup LabGenius Raises $10M

The London-based startup LabGenius announced that they raised over $10 million in Series A Funding. They are a drug discovery company that utilizes artificial intelligence (AI), robotic automation, and synthetic biology. Their main focus is to find novel protein therapeutics. 

According to Dr James Field, CEO and Founder of LabGenius, “Protein therapeutics have an unparalleled potential to both treat disease and alleviate human suffering. By transforming how these drugs are discovered, we have a shot at improving the lives of countless people. Being able to robustly engineer novel therapeutic proteins has immense commercial and societal value. The discovery of protein therapeutics has historically been highly artisanal, relying heavily on humans for both experimental design and execution. This dependence has proved limiting because, as a species, we’re cognitively incapable of fully grasping the complexity of biological systems.”

The Series A investment round is led by Lux Capital and Obvious Ventures. Other participants included Felicis Ventures, Inovia Capital, Air Street Capital, and other existing investors. CEO and founder of Recursion Pharmaceuticals, Chris Gibson, along with Inovia Capital General Partner Patrick Pichette, are also investing. Pichette is the former CFO of Google. 

According to the company, they will use the capital to “scale their team, expand the scope of its discovery platform, and initiate an internal asset development program.” One of their main goals is to evolve novel antibody fragments. These could be used to treat certain conditions that can’t rely on conventional antibody formats. 

Lux Capital and Obvious Ventures

Zavain Dar, Partner at Lux Capital, along with Nan Li, Managing Director at Obvious Ventures, have been involved in the life science startup environment for some time. Their investment strategy dates back nine years, including a 2013 investment into Zymergen, a molecule discovery and manufacture company based out of California. In 2016, they were involved in Recursion Pharmaceuticals, who went on to a series C raise of $156 million in July. 

Their strategy follows a path, starting at industrial biotech technology with Zymergen and followed by root-cause biology discovery with Recursion Pharmaceuticals. It is closed out by creating composition of matter and IP with LabGenius.  

Dar explained his reasoning behind choosing LabGenius over other startups. 

“We scoured the globe, and didn’t want to be constrained by what happened to be in our backyard,” he says. “They are leading the pack…and now with backing and pharma partnerships, should be in a good position.”

Humans No Longer Sole Agents of Innovation

When speaking to TechCrunch, Dr James Field said, “My central thesis, the thing that’s really the driving force behind the company, is the conviction that we’re entering an age in which humans will no longer be the sole agents of innovation. Instead, new knowledge, technologies and sophisticated real-world products will be invented by smart robotic platforms called empirical computation engines. An empirical computation engine is an artificial system capable of recursively and intelligently searching a solution space.”

The company has created a discovery platform called EVA, and it integrates multiple new technologies coming from different fields including artificial intelligence. After discovery and characterisation, LabGenius then sends their proprietary molecules to clinics. 

Field explains the company’s EVA technology as a “machine learning-driven, robotic platform”,” that is capable of “designing, conducting and critically learning from its own experiments.” 

“For decades, scientists, engineers and technologists have dreamt of building ‘robot scientists’ capable of autonomously discovering new knowledge, technologies and sophisticated real-world products,” says Field.

“For protein engineers, that dream has now entered the realm of possibility. The rapid pace of technological development across the fields of synthetic biology, robotic automation and ML has given us access to all the essential ingredients required to create a smart robotic platform capable of intelligently discovering novel therapeutic proteins.”

 

Spread the love

Deep Learning Specialization on Coursera
Continue Reading