Last week, the $1 million Turing Award — sometimes called the “Nobel Prize of Computing” — was awarded to three pioneers in artificial intelligence: Yann LeCun, Geoffrey Hinton, and Yoshua Bengio.

There’s a cool story behind the work they did.

In the 1980s, researchers briefly got excited about the concept of neural networks, an approach to artificial intelligence that, as the name suggests, resembles how the human brain works. The idea was that rather than following carefully specified rules, neural networks could “learn” the way humans do — by looking at the world. They could start out without preprogrammed preconceptions and make inferences from the data about how the world works and how to work in it.

But after several years of research, the field couldn’t get anywhere with neural net approaches. The hoped-for learning behavior didn’t really materialize, and they underperformed other strategies for AI, like explicitly programming the AI with logical rules to follow. So by the 1990s, the field had moved on.

Hinton, LeCun, and Bengio, though, never really gave up on the idea. They kept tinkering with neural nets. They made substantial improvements on the original concept, including adding “layers” — a structure for organizing the “neurons” in a neural net that significantly improves performance. And eventually, it turned out that neural nets were as powerful a tool as we could have hoped for; they just need powerful supercomputers, and tons of data, to be useful.

We didn’t have computers powerful enough to take advantage of neural nets until early this decade. When we developed those computers, the neural net AI breakthroughs started. Suddenly, AI and neural nets could be used for image recognition. For translation. For voice recognition. For game-playing. For biology research. For generating text that reads almost as if it were written by a human.

We began to invent different ways of configuring neural nets so that we could get better results from them. For example, to make photorealistic pictures of humans that never existed, you actually train two neural nets: One learns to draw pictures, and the other learns to judge between machine-drawn pictures and real-life ones.

The paradigm LeCun, Hinton, and Bengio had stubbornly kept working on became the biggest game in town. Today, LeCun is vice president and chief AI scientist at Facebook. Hinton works for Google Brain and the University of Toronto. Bengio founded a research center at the University of Montreal.

And worldwide, thousands of researchers work on neural nets, hundreds of billions of dollars have been invested in hundreds of AI startups, and we keep discovering new applications. There’s no question the Turing Award is richly deserved — rarely does an idea take a field by storm like this.

Watching the field of AI be transformed raises questions about where it’s headed

There’s another way the field of artificial intelligence has been transformed in the past 10 years: Concerns about the societal effects of artificial intelligence are now being taken much more seriously.

There are many possible reasons for that, of course, but one driving factor is the pace of progress in AI over the past decade. Ten years ago, many people felt confident in asserting that truly advanced AI, the kind we had to worry about, was centuries away.

Now, AI systems powerful enough to raise ethical questions are already here, and it’s no longer clear how distant general AI — AI that surpasses human capabilities across many domains — is.

LeCun, Bengio, and Hinton all take AI ethics concerns quite seriously, though they stop short of endorsing fears that their creation will wipe us off the earth. (Hinton’s stance, the most pessimistic of the three, is that nuclear war or a global pandemic will probably get there first.)

“If we had had the foresight in the 19th century to see how the Industrial Revolution would unfold,” Bengio says in his chapter of the 2018 book Architects of Intelligence, “maybe we could have avoided much of the misery that followed. ... The thing is, it’s going to take probably much less than a century this time to unfold that story, and so the potential negative impacts could be even larger. I think it’s really important to start thinking about it right now.”

Witnessing the astounding rush of progress this decade is enough to instill caution — and leave us with a lot of uncertainty about what to expect next. A paradigm that many had dismissed as irrelevant turned out, once we had good enough computers, to be an incredibly powerful tool. New applications and new variations were discovered. It’s enough to make you wonder whether that could happen again.

Are there other AI techniques that most researchers aren’t paying attention to but that will break through once computers get better and we finally have tools powerful enough to take advantage of them? Will we keep inventing variants of neural nets that make once-unsolved problems look easy?

It’s hard to predict. But seeing the field totally transformed in the space of a decade gives a sense of how fast, startling, and unpredictable progress can be.

Source: Vox

Connecticut Innovations Inc. (CI), the State of Connecticut's investment arm, has completed an initial $500,000 investment in Israel-based non-standard speech recognition startup Voiceitt, the parties announced Monday. The investment is part of CI’s VentureClash, an investment prize Voiceitt has won in 2018, which has a maximum investment potential of $1.5 million. Founded in 2012 and based in Ramat Gan, a town in the Tel Aviv area, Voiceitt, registered as Technologies of Voice Interface Ltd., develops technology that helps people with irregular speech patterns, caused by illness or other medical conditions, including ALS, autism and Parkinson's disease, communicate with others.

Source: CTECH

Graphic processing units (GPUs) are being increasingly termed as the new computer processing units (CPUs) in the era of cutting-edge technologies along the likes of AI, big data and machine learning (ML). Indeed, a general consensus among industry experts is that these 3D graphics cards have turned around the fortunes of a fairly dull personal computer market as the world witnessed widespread adoption of the 32-bit operating systems in the mid-90s.

The global GPU market,slated to accrue $80bn by 2024 by Global Market Insights, has come a long way since its inception, owing to the fast-paced evolution of modern graphics processor since the introduction of the first 3D add-in graphics card in 1995. In the current times, GPU-powered chips are revolutionizing a plethora of business verticals and are adding a unique dimension to the way technology is being developed and applied.

The increasing role of GPU-powered AI platforms in honing autonomous vehicle technology

Owing to its cost-efficiency and immense popularity, GPU has emerged as the most dominant chip architecture for self-driving technology in the recent past. The increasing complexities of computing hardware and the requirements for testing autonomous cars on real roads warrant superior AI-based operating platforms that would anticipate potential hazards while driving. In this regard, world's foremost GPU maker Nvidia has been scoring big wins in terms of developing GPU-powered AI platforms and teaming up with well-known automotive giants.

After introducing its original AI-based supercomputer platform Drive PX in 2015, the US-based Nvidia has recently come up with a new iteration of the platform named Pegasus which can be utilized to power Level 5 autonomy. This new platform is able to support fully autonomous cars without pedals, steering wheels or mirrors. It has been built on Nvidia's CUDA GPUs which has intensified its computing speed by 10 times and lowered the power consumption by 16 times.

Equipped with these Level 5-empowering GPUs, the driverless cars would most likely be deployed in a ride-hailing capacity in restricted settings like airports or college campuses. Moreover, it has also been reported that German engineering and electronics company Robert Bosch GmbH and leading automaker Daimler AG have partnered up with Nvidia to utilize its Pegasus system as the platform for their self-driving vehicle designs beginning in 2020. A few other automotive firms such as Zenrin, ZF and Audi have committed to use the AI-based computers of Nvidia.

Considering the instances of GPU makers building new products, particularly Nvidia, it can certainly be claimed that the criticality of GPU-powered AI platforms in the effective implementation of autonomous vehicles programs is of much significance. Many more such developments are in the works and would speed up the creation of AI-driven big data systems, in which GPUs would play a pivotal role in the upcoming years.

Transforming the IT infrastructure of healthcare sector with high-end graphic processors

Bearing in mind the way recent economic trends have been unfolding, it is quite difficult to quantify and measure the impact of new-age technologies such as IoT, AI, machine learning and deep learning on the product offerings and growth of almost all business verticals. However, it is certain that the ability of these technologies to identify common patterns for decision making and make sense of big data can be rightly termed as awe-inspiring. Out of these technologies, deep learning is one of the most significant branches of AI which is taking the world by storm with its ability to successfully interpret and provide crucial insights from massive amounts of data.

In this context, it is quite imperative to take note of the fact that healthcare institutions across the globe are churning out data on a scale that it simply is too humongous for manual processing to be a feasible method. In combination with graphics processors, which have now evolved to offer extremely high processing power, deep learning is transforming medical research, treatment, and patient services by turning the 'black box' of big data into effective solutions.

While taking stock of the current scenario pertaining to the AI in healthcare, it is particularly essential to mention that tech giants such as Google, Apple and Tencent are creating GPU-accelerated products to improve the predictive analytics domain of global healthcare regime. These firms have already embarked upon major projects that would utilize advanced graphic processors in their AI software to strengthen neural networks and speed up the pace of these machines in learning and improving automatically. Moreover, these initiatives would assist healthcare organizations in streamlining the huge amounts of data being generated in the present times.

Elaborating on a few of such initiatives, Google has recently acquired DeepMind's healthcare division to build "an AI assistant for physicians and nurses". This is in addition to the tech behemoth's quest to improve accessibility of electronic medical records (EMR) which is likely to become a crucial element in disrupting the healthcare landscape using AI.

Amazon is another prominent firm that is hoping to decode the information in haphazard writing like medical records or even physician's notes. In 2018, thee-commerce giant unveiled a new ML service Amazon Comprehend Medical that would use natural language processing to interpret the medical records.

Given the sheer ML and engineering prowess of Google and the willingness of other tech leaders like Amazon to build new products with help of GPU-powered AI platforms, the healthcare sector is on the cusp of witnessing a change from early diagnosis to medical records, to predicting the outcomes of numerous complex treatments beforehand.

For most computer users, GPUs are remnant of the video cards that were designed for high-end, graphic intensive games. These were solely optional, which did not impact the buying decision of an average user investing in a server or personal computer. However, these specialized processors have now carved out a new place for themselves in the computing world and are powering the technology which supports the development of autonomous cars, speech recognition, cancer diagnosis and numerous other intelligent use cases.

Source: Innovation Enterprise

In the future, artificial intelligence will probably reshape our economy, society and our lives. But achieving AI’s full potential will not only require many technological innovations but research into some societal difficulties, including ethical issues, workplace disruptions, and changing human interactions.

The technology's impact on those human interactions has attracted a number of experts. Yale professor Nicholas Christakis made some points in a recent article in the Atlantic, How AI Will Rewire Us.

Sci-fi movies portray artificial intelligence as self-aware computers, evil robots, and digital armageddon. Some fear that in the future, almighty superintelligent AI will far surpass human intelligence, posing an existential threat to humanity. But, the real threat to humanity, said Prof. Christakis, is that “for better and for worse, robots will alter humans’ capacity for altruism, love, and friendship.”

Major innovations have long had an impact on the ways that people interact with each other and the printing press, telephone, radio, TV, and the internet are such technologies.

“As consequential as these innovations were, however, they did not change the fundamental aspects of human behavior that comprise what I call the social suite: a crucial set of capacities we have evolved over hundreds of thousands of years, including love, friendship, cooperation, and teaching…” he wrote.

“But adding AI to our midst could be much more disruptive. Especially as machines are made to look and act like us and to insinuate themselves deeply into our lives, they may change how loving or friendly or kind we are --not just in our direct interactions with the machines in question, but in our interactions with one another.”

Artificial intelligence can both improvise how humans relate to one another as well as make them behave less ethically. 

Experiments with hybrid groups of people and robots working together have shown that the right kind of AI can help improve the group’s overall performance. But, in other experiments, he found that by adding a few bots posing as selfish humans, the same groups that previously behaved in an unselfish, generous way toward each other were now driven by the bots to behave in a selfish way. 

This shouldn’t be surprising, as over the last few years we’ve seen how the spread of false information by malicious bots over social media can have a highly negative, polarizing impact on large groups of people.

“As AI permeates our lives, we must confront the possibility that it will stunt our emotions and inhibit deep human connections, leaving our relationships with one another less reciprocal, or shallower, or more narcissistic,” he writes.

We will be needing rules and policies overlooking to help us deal with potentially negative impacts of AI on society, not unlike how we’ve stopped corporations from polluting our water supply or individuals from spreading harmful cigarette smoke.

“In the not-distant future, AI-endowed machines may, by virtue of either programming or independent learning (a capacity we will have given them), come to exhibit forms of intelligence and behavior that seem strange compared with our own,” concludes Prof. Christakis. “We will need to quickly differentiate the behaviors that are merely bizarre from the ones that truly threaten us."

The article first appeared on The Wall Street Journal


PathAI said April 17 that it raised $60 million in Series B funding led by General AtlanticGeneral Catalyst invested in the round and other existing investors. PathAI, of Boston, provides artificial intelligence-powered technology for pathology.


PathAI Secures $60M in Series B Funding Led by General Atlantic and Existing Investor General Catalyst
Computational Pathology Leader Plans to Accelerate Industry Impact

Boston, Mass. – April 17, 2019 – PathAI, a global provider of artificial intelligence-powered technology for pathology, announced today that it has raised $60 million in Series B funding. The round was led by new investorGeneral Atlantic, a leading global growth equity firm, with strong participation from General Catalyst and other existing investors. The new capital will fuel PathAI’s continued expansion as the company seeks to advance the medical discipline of pathology. General Atlantic Managing Director Dr. Michelle Dipp will be joining PathAI’s board of directors as the company advances its mission of offering faster, safer, and more powerful solutions for the diagnosis and sub-typing of diseases like cancer.

PathAI plans to use this new capital to enhance offerings to existing partners, drive continuous improvement of its flagship pathology research platform, meet market demands, and fuel research and development into new tools and medical devices. Its growing partnerships with leading global pharmaceutical companies seek to accelerate drug development in life-saving therapeutics, and its partnerships with leading diagnostic laboratories aim to support pathologists in bringing faster, more accurate, and more predictive diagnostics to patients.

“Our goal has been clear since day one – a relentless drive to ensure patients get the right diagnosis and the most effective treatment. We’re looking forward to working with our partners to scale this effective approach across disease areas and around the world,” PathAI co-founder and Chief Executive Officer Dr. Andy Beck said. “The global network and deep expertise in technology and life sciences brought by General Atlantic, alongside the continued support of existing investors like General Catalyst, can only enhance our ability to effect change toward this major, impactful objective.”

“PathAI is a clear leader in the emerging digital pathology industry that is being disrupted by technology and machine learning,” said Dr. Michelle Dipp. “As we remain committed to supporting the next generation of life sciences companies, we’re thrilled to partner with PathAI, which seeks to address critical problems by bringing cutting edge storage, viewing, and AI-enabled analytics to help pathologists make fast, accurate, and consistent diagnoses.

“PathAI’s work could radically improve the accuracy and reproducibility of disease diagnosis and support the development of new medicines to treat those diseases,” said David Fialkow, Managing Director at General Catalyst. “GC is honored to once again back founders, Andy Beck and Aditya Khosla, Chief Business Officer Tiffany Freitas, Chairman Jeff Leiden, and the entire PathAI team. The positive – and global – impact of getting this right cannot be overstated. We think this is the team that has all the potential to bring these game-changing solutions to market.”


This funding round comes as PathAI has secured critical certifications in quality management and information security systems. PathAI’s staff has grown from 25 to more than 60 in the past year, reflecting the company’s investments in machine learning, product development, quality and regulatory affairs, and scientific program management. PathAI has been named one of the best places to work in Boston by the Boston Business Journal and Built in Boston, and key partners like Bristol-Myers Squibb and Novartis have publicly lauded the work they have done with PathAI to advance the state of disease treatment.

About PathAI

PathAI is a leading provider of AI-powered research tools and services for pathology. PathAI’s platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit

About General Atlantic

General Atlantic is a leading global growth equity firm providing capital and strategic support for growth companies. Established in 1980, General Atlantic combines a collaborative global approach, sector specific expertise, a long-term investment horizon and a deep understanding of growth drivers to partner with great entrepreneurs and management teams to build exceptional businesses worldwide. General Atlantic has more than 150 investment professionals based in New York, Amsterdam, Beijing, Greenwich, Hong Kong, Jakarta, London, Mexico City, Mumbai, Munich, Palo Alto, São Paulo, Shanghai, and Singapore. For more information on General Atlantic, please visit the website:

About General Catalyst

General Catalyst is a venture capital firm with approximately $5B raised to date that makes early-stage and transformational investments. We back fearless entrepreneurs who have the potential to build market-leading technology companies like Airbnb, BigCommerce, ClassPass, Datalogix, Datto, Demandware, Gusto, HubSpot, KAYAK, Oscar, Snap, Stripe, and Warby Parker. With offices in San Francisco, Palo Alto, New York City and Boston, our portfolio companies benefit from a bicoastal network of talent, customers, and opportunity. For more:

Source: PE Hub Network

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