Herzliya, Israel-based Pangea announced today a platform and a process with which governments worldwide can issue a smart card that facilitates entry into airport terminals and airplanes. The card has the holder’s photo, a digital signature, a chip, and a hologram, includes up-to-date encrypted data on the holder’s Covid-19 profile, and can be securely linked to a country’s medical database.
Read more here
“It is impossible to defeat an enemy that we cannot see,” says Bill Gates. Like many other observers and participants in the fight with COVID-19, Gates maintains that testing is critical for the reopening of the US economy, to identify new hot spots and intervening, even reversing relaxed policies, in a timely manner.
Read more here
News from Israel today about rapid progress in developing a COVID-19 vaccine and in identifying people at the highest risk of severe COVID-19 complications.
Read more here
As the coronavirus came suddenly out of stealth mode, displaying a record-breaking adherence to Silicon Valley’s mantra of “scaling up,” shocked startup investors—watching the Bloomberg US Startups Barometer plunging more than 50% in 3 months—ask “what’s to be done now”?
Read more here
Recent surveys, studies, forecasts and other quantitative assessments of the progress and impact of AI highlight the confusion and contradictory attitudes of consumers about the privacy of their data, the impact of AI on jobs, and the race for AI supremacy.
Read more here
The year 2020 has been featured in many predictions and long-term visions in the past, implying not only the terminal point for the forecast or planning period but also a crystal-clear crystal ball. Now that the year 2020 is our present, we can clearly see where these prognostications went wrong and try to understand why they were so cloudy.
Read more here
Recent surveys, studies, forecasts and other quantitative assessments of the progress of AI highlight the increasing presence of AI in the healthcare industry, the assistance AI may provide in the future to workers’ cognitive tasks, and the continuing acceleration in data production and dissemination.
Source: CB Insights
Read more here
Yoshua Bengio speaking with Karen Hao at the EmTech MIT conference, September 18, 2019
[September 20, 2019]
Asked what is the biggest misconception about AI, Yoshua Bengio answered without hesitation “AI is not magic.” Winner of the 2018 Turing Award (with the other “fathers of the deep learning revolution,” Geoffrey Hinton and Yann LeCun), Bengio spoke at the EmTech MIT event about the “amazing progress in AI” while stressing the importance of understanding its current limitations and recognizing that “we are still very far from human-level AI in many ways.”
Deep learning has moved us a step closer to human-level AI by allowing machines to acquire intuitive knowledge, according to Bengio. Classical AI was missing this “learning component,” and deep learning develops intuitive knowledge “by acquiring that knowledge from data, from interacting with the environment, from learning. That’s why current AI is working so much better than the old AI.”
At the same time, classical AI aimed to allow computers to do what humans do—reasoning, or combining ideas “in our mind in a very explicit, conscious way,” concepts that we can explain to other people. “Although the goals of a lot of things I’m doing now are similar to the classical AI goals, allowing machines to reason, the solutions will be very different,” says Bengio. Humans use very few steps when they reason and Bengio contends we need to address the gap that exists between our mind’s two modes of thought: “System 1” (instinctive and emotional) and “system 2” (deliberative and logical). This is “something we really have to address to approach human-level AI,” says Bengio.
To get there, Bengio and other AI researchers are “making baby steps” in some new directions, but “much more needs to be done.” These new directions include tighter integration between deep learning and reinforcement learning, finding ways to teach the machine meta-learning or ”learning to learn”—allowing it to generalize better, and understand better the causal relations embodied in the data, going beyond correlations.
Bengio is confident that AI research will overcome these challenges and will achieve not only human-level AI but will also manage to develop human-like machines. “If we don’t destroy ourselves before then,” says Bengio, “I believe there is no reason we couldn’t build machines that could express emotions. I don’t think that emotions or even consciousness are out of reach of machines in the future. We still have a lot to go… [to] understand them better scientifically in humans but also in ways that are sufficiently formal so we can train machines to have these kinds of properties.”
At the MIT event, I talked to two Intel VPs—Gadi Singer and Carey Kloss—who are very familiar with what companies do today with the current form of AI, deep learning, with all its limitations. “Enterprises are at a stage now where they have figured out what deep learning means to them and they are going to apply it shortly,” says Singer. “Cloud Service Providers deploy it at scale already. Enterprise customers are still learning how it can affect them,” adds Kloss.
Many of these companies have been using for years machine learning, predictive analytics, and other sophisticated techniques for analyzing data as the basis for improving decision-making, customer relations, and internal processes. But now they are figuring out what deep learning, the new generation of machine learning, can do for their business. Singer has developed what he calls the “four superpowers framework” as a way of explaining what’s new about deep learning from a practical perspective, the four things deep learning does exceptionally well.
Deep learning is very good at spotting patterns. It first demonstrated this capability with its superior performance in analyzing images for object identification, but this exceptional capability can be deployed to other types of data. While traditional machine learning techniques have been used for years in fraud detection, for example, deep learning is very powerful in “identifying remote instances of a pattern,” says Singer.
The second “superpower” is being a universal approximator. Deep learning is very good at mimicking very complex computations with great accuracy and at a fraction of the power and time of traditional computation methods. “Whatever you can accelerate by 10,000x might change your business,” says Singer.
Sequence to sequence mapping is the third exceptional deep learning capability. An example would be real-time language translation. Previously, each word was translated in isolation but deep learning brings the “depth of context,” adding a time dimension by taking into account the entire sequence of words.
Last but not least is generation based on similarities. Once a deep learning model learns how a realistic output looks like, it can generate a similar one. Generating images from text is an example. Another one is WaveNet, a speech generation application from Google, mimicking the human voice. Yet another example is medical records anonymization, allowing for privacy-preserving sharing, research, and analysis of patient records.
EmTech 2019 also featured MIT Technology Review’s recent selection of “35 innovators under 35.” A few of these innovators got on the list because they developed and demonstrated a number of practical and successful applications of deep learning. These included Liang Xu and his AI platform that helps cities across China improve public health, reduce crime, and increase efficiency in public management; Wojciech Zaremba, using deep learning and reinforcement learning to train a robot hand to teach itself to pick up a toy block in different environments; and Archana Venkataraman who developed a deep learning model that can detect epileptic seizures and, as a result, limit invasive monitoring and improve surgical outcomes.
There is no doubt that Bengio and Hinton and LeCun have created in deep learning a tool with tremendous positive social and economic value, today and in the future. But they—and other AI researchers—insist on the ultimate goal being the creation of “human-level intelligence” or even human-like machines. Why do these experts in machine learning refuse to learn from history, from seven decades of predictions regarding the imminent arrival of human-level intelligence leading only to various “AI winters” and a lot of misconceptions, including unfounded fear and anxiety about AI? And why aren’t goals such as curing diseases, eliminating hunger, and making humans more productive and content sufficient enough to serve for them as motivating end-goals?
Originally published on Forbes.com
[September 27, 2019]
Language is imprecise, vague, context-specific, sentence-structure-dependent, full of fifty shades of gray (or grey). It’s what we use to describe progress in artificial intelligence, in improving computers’ performance in tasks such as accurately identifying images or translating between languages or answering questions. Unfortunately, vague or misleading terms can lead to inaccurate and misleading news.
Earlier this month we learned from the New York Times that “…in just the past several months researchers have made significant progress in developing A.I. that can understand languages and mimic the logic and decision-making of humans.” The NYT article reported on “A Breakthrough for A.I. Technology” with the release of a paper by a team of researchers at the Allen Institute for Artificial Intelligence (AI2), summarizing their work on Aristo, a question answering system. While 3 years ago the best AI system scored 59.3% on an eight-grade science exam challenge, Aristo recently correctly answered more than 90% of the non-diagram, multiple choice questions on an eighth-grade science exam and exceeded 83% on a 12th-grade science exam.
No doubt this a remarkable and rapid progress for the AI sub-field of or Natural Language Understanding (NLU) or more specifically, as the AI2 paper states, “machine understanding of textbooks…a grand AI challenge that dates back to the ’70s.” But does Aristo really “reads,” “understands” and “reasons” as one may understand from the language used in the paper and similar NLU papers?
“If I could go back to 1956 [when the field of AI was launched], I would choose a different terminology,” says Oren Etzioni, CEO of AI2. Labeling “anthropomorphizing” this “unfortunate history,” Etzioni clearly states his position about the language of AI researchers:
“When we use these human terms in the context of machines that’s a huge potential for misunderstanding. The fact of the matter is that currently machines don’t understand, they don’t learn, they aren’t intelligent—in the human sense… I think we are creating savants, really really good at some narrow task, whether it’s NLP or playing GO, but that doesn’t mean they understand much of anything.”
Still, “human terms,” misleading or not, is what we have to describe what AI programs do, and Etzioni argues that “if you look at some of the questions that a human would have to reason his or her way to answer, you start to see that these techniques are doing some kind of rudimentary form of reasoning, a surprising amount of rudimentary reasoning.”
The AI2 paper elaborates further on the question “to what extent is Aristo reasoning to answer questions?” While stating that currently “we do not have a sufficiently fine-grained notion of reasoning to answer this question precisely,” it points to a recent shift in the understanding by AI researchers of “reasoning” with the advent of deep learning and “machines performing challenging tasks using neural architectures rather than explicit representation languages.”
Similar to what has happened recently in other AI sub-fields, question answering has gotten a remarkable boost with deep learning, applying statistical analysis to very large data sets, finding hidden correlations and patterns, and leading to surprising results, described sometimes in misleading terms.
What current AI technology does is “sophisticated pattern-matching, not what I would call ‘understanding’ or ‘reasoning,’” says TJ Hazen, Senior Principal Research Manager at Microsoft Research.* Deep learning techniques, says Hazen, “can learn really sophisticated things from examples. They do an incredible job of learning specific tasks, but they really don’t understand what they’re learning.”
What deep learning and its hierarchical layers of complex calculations, plus lots of data and compute power, brought to NLU (and other AI specialties) is unprecedented level of efficiencies in designing models that “understand” the task at hand (e.g., answering a specific question). Machine learning used to require deep domain knowledge and a deep investment of time and effort in coming up with what its practitioners call “features,” the key elements of the model (called “variables” in traditional statistical analysis—professional jargon being yet another challenge for both human and machine language understanding). By adding more layers (steps) to the learning process and using vast quantities of data, deep learning has taken on more of the model design work.
“Deep learning figures out what are the most salient features,” says Hazen. “But it is also constrained by the quality and sophistication of the data. If you only give it simple examples, it’s only going to learn simple strategies.”
AI researchers, at Microsoft, AI2, and other research centers, are aware of deep learning’s limitations when compared with human intelligence, and most of their current work, while keeping within the deep learning paradigm, is aimed at addressing these limitations. “In the next year or two,” says Etzioni, “we are going to see more systems that work not just on one dataset or benchmark but on ten or twenty and they are able to learn from one and transfer to another, simultaneously.”
Jingjing Liu, Principal Research Manager at Microsoft Research also highlights the challenge of “transfer learning” or “domain adaptation,” warning about the hype regarding specific AI programs’ “human parity.” Unlike humans that transfer knowledge acquired in performing one task to a new one, a deep learning model “might perform poorly on a new unseen dataset or it may require a lot of additional labeled data in a new domain to perform well,” says Liu. “That’s why we’re looking into unsupervised domain adaptation, aiming to generalize pre-trained models from a source domain to a new target domain with minimum data.”
Real-world examples, challenges, and constraints help researchers address the limitations of deep learning and offer AI solutions to specific business problems. A company may want to use a question answering system to help employees find what they need in a long and complex operations manual or a travel policy document.
Typically, observes Hazen, the solution is a FAQ document, yet another document to wade through. “Right now, most enterprise search mechanisms are pretty poor at this kind of tasks,” says Hazen. “They don’t have the click-through info that Google or Bing have. That’s where we can add value.” To deploy a general-purpose “reading comprehension” model in a specific business setting, however, requires successful “transfer learning,” adapting the model to work with hundreds of company-specific examples, not tens of thousands or even millions of examples.
Microsoft researchers encounter these real-world challenges when they respond to requests from Microsoft’s customers. A research institute such as AI2 does not have customers so it created a unique channel for its researchers to interact with real-world challenges, the AI2 Incubator, inviting technologists and entrepreneurs to establish their startups with the help of AI2 resources. Lexion.ai is one of these startups, offering NLU software that organizes and reads contracts, and extracts the specific terms employees need for their work.
Unfortunately, human ambition (hubris?) hasn’t stopped at solving specific human challenges as sufficient motivation for AI research. Achieving “human-level intelligence” has been the ultimate goal for AI research for more than six decades. Indeed, it has been an unfortunate history, as a misleading goal has led to misleading terms which in turn lead to unfounded excitement and anxiety.
Fortunately, many AI researchers continue to expand what computers could do in the service of humanity. Says TJ Hazen: “I prefer to think about the work I’m doing as something that will help you do a task but it may not be able to do the full task for you. It’s an aid and not a replacement for your own capabilities.” And Oren Etzioni: “My favorite definition of AI is to think of it as Augmented Intelligence. I’m interested in building tools that help people be much more effective.”
*Opinions expressed by Microsoft’s researchers do not necessarily represent Microsoft’s positions.
Originally published on Forbes.com
[April 29, 2019]
Opening the CannaTech conference earlier this month, former Israeli prime minister Ehud Barak quipped that Israel is now the “land of milk, honey and cannabis.” Given the recent performance of the cannabis-related stocks traded on the Tel-Aviv stock exchange (Barak is Chairman of InterCure whose stock appreciated 1000% in 2018), are investors getting high on nothing more than a buzz bubble?
Behind the buzz about “marijuana millionaires,” Yuge market potential, and volatile stocks (InterCure’s stock nearly tripled earlier this year but is now 25% off its peak), is a serious 55-year-old Israeli enterprise of pioneering interdisciplinary research into the medical benefits of cannabis. Supported by a perfect climate for growing cannabis, it has led to a very supportive climate—academic, regulatory, and entrepreneurial—for developing botanical-sourced pharmaceutical-grade products. Like the rest of the world, Israel has considered cannabis (and still does) to be a “dangerous drug,” but unlike the rest of the world, it has not let the stigma deter its insatiable curiosity about cannabis’s therapeutic potential.
The entrepreneurial poster child for this long-held belief in the efficacy of medical marijuana is Breath of life (BOL) Pharma. Founded in 2008, today it is “the only company in Israel that is fully integrated throughout the value chain,” says its CEO, Tamir Gedo.
This means BOL Pharma is compliant with the GAP and GMP standards of the global pharmaceutical industry, governing all stages of production and distribution, from cultivation to processing to marketing of finished products such as tablets, capsules, inhalers, creams and oils. This unique competitive advantage is buttressed by BOL’s R&D function, currently involved with 32 Phase 2 clinical trials, and a 65,000 square feet production plant and one million square feet of cultivation facilities.
“You don’t see this kind of consistency in products around the world,” says Gedo. “Flowers are not consistent and if you don’t have consistency, you run the risk of having side effects at different times.” The need to overcome the challenge of developing medicine from an inconsistent botanical source is why 60% of BOL’s 200 employees have worked before in the pharmaceutical industry and why Gedo insists on staying focused on the company’s medical cannabis vision and not developing products for recreational use. “Our advantage is time,” says Gedo, “we’ve been doing it for many years.”
This time- and experience-based competitive advantage applies to the Israeli cannabis ecosystem as a whole. In the early 1960s, looking to make his mark in the academic world, Israeli chemist Raphael Mechoulam decided to focus on cannabis research because “in a small country like Israel, if you want to do significant work, you should try to do something novel.” Moreover, “a scientist should find topics of importance,” he says in the documentary The Scientist. “Cannabis had been used for thousands of years both as a drug [and] as a recreational agent, but surprisingly, the active compound was never isolated in pure form.”
Mechoulam and his colleagues isolated the chemical compounds of cannabis (which he called “cannabinoids”), specifically CBD (the main non-psychoactive component) and THC (the psychoactive component). In the early 1990s, they discovered the endocannabinoid system in the human body which is involved in regulating a variety of physiological and cognitive processes (including mood and memory), and in mediating the pharmacological effects of cannabis.
These discoveries have led to a vast body of research conducted in Israel and around the world on various aspects, medical and otherwise, of cannabinoids (see here, for example). With government support, both in terms of funding and regulation, Israel has become a center for medical cannabis R&D, with many academic institutions and companies “offshoring” research and clinical trials to Israel, having been prevented from doing it in the US and elsewhere.
Gedo calls this R&D-and-clinical-trials-as-a-service “open innovation,” providing the research and regulatory infrastructure for others to innovate and produce their own IP. But the infrastructure and accumulated experience and expertise also help BOL Pharma and other Israeli companies develop their own unique cannabis-related IP. For example, BOL has been working on unique new formulations which make the medical cannabis more effective by increasing its “bio-availability” (rate of absorption in the body), thus reducing the cost to the consumer and potential side effects.
When BOL entered the medical cannabis market a decade ago it did not have a lot of local (or global, for that matter) competition. Today, according to a recent survey published in Israeli business publication Globes, there are more than 100 Israeli companies contributing to the “current boiling point” of this market. These include companies growing and processing cannabis, or running pharma production facilities, or exporting Israeli know-how, or developing drug delivery mechanisms.
These companies are going after a worldwide medical cannabis market estimated to grow rapidly to $100.03 billion in 2025, according to Grand View Research. Most are private companies, but some may test the public markets before long—BOL Pharma may list on the Canadian stock exchange or in the US and Canndoc, another pharma-grade medical cannabis pioneer (acquired last year by InterCure), has recently submitted a confidential prospectus for a Nasdaq IPO.
The ever-growing market size estimates and increased activity in the public markets have drawn the attention of venture capital firms. Funding for cannabis startups in the US more than doubled from 2017 to 2018, reaching more than $1.3 billion, according to Crunchbase. The first quarter of 2019 saw funding more than double year-over-year, and earlier this month, Pax Labs (vaporization technologies and devices) raised $420 million at a valuation of $1.7 billion.
The most recent funding data for cannabis-related Israeli startups shows that only $76 million have been raised from 2013 to 2017, according to IVC Research. That number has probably increased considerably by now as just one cannabis-related startup, Syqe Medical (inhalers), has raised $50 million in its second round of funding at the end of 2018.
And there’s more to come, including Israel-US collaborations. OurCrowd, Israel’s most active venture investor (including in Syqe Medical), announced in January that it will partner with Colorado-based 7thirty to create a new $30 million fund focused on emerging cannabis technology companies in Israel, Canada and the United States.
At its annual conference last month, OurCrowd awarded 88-year-old Professor Raphael Mechoulam its Maimonides Lifetime Achievement award (the other winner was 100-year-old Professor Avraham Baniel, the inventor and co-founder of DouxMatok). In accepting the award, Mechoulam talked about his current work, predicting that “within the next decade, maybe less, we shall have drugs for a variety of diseases based on the compounds, the constituents of the [cannabis] plant and the constituents of our own body, the endogenous cannabinoids, and compounds that we make, that will be effective for a large number of diseases.”
Originally published on Forbes.com
