Archive For The “Artificial Intelligence” Category

Israel AI Medical Startup Aidoc Gets FDA Approval

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Israel AI Medical Startup Aidoc Gets FDA Approval

Israeli startup Aidoc, a developer of artificial intelligence (AI)-powered software that analyzes medical images, announced on Wednesday that it received Food and Drug Administration (FDA) clearance for a solution that flags cases of Pulmonary Embolism (PE) in chest scans for radiologists.


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Portions of this article were originally reported in NoCamels.com


Aidoc has CE (Conformité Européenne) marking for the identification and triage of pulmonary embolism (PE) in CT pulmonary angiograms, and FDA approval to scan images for brain hemorrhages.

The latest approval came a month after Aidoc secured $27 million in a Series B round led by Square Peg Capital. Founded in 2016 by Guy Reiner, Elad Walach, and Michael Braginsky, the company has raised some $40 million to date.

Aidoc’s technology assists radiologists in expediting problem-spot detection through specific parameters such as neuron-concentration, fluid-flow, and bone-density in the brain, spine, abdomen, and chest. Aidoc says its solutions cut the time from scan to diagnosis for some patients from hours to under five minutes, speeding up treatment and improving prognosis.

“What really excites us about this clearance is that it paves the way towards scalable product expansion,” Walach, who serves as Aidoc CEO, said in a statement. “We strive to provide our customers with comprehensive end-to-end solutions and have put a lot of effort in developing a scalable AI platform.”

Walach said the company has eight more solutions in active clinical trials.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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Facebook Creates New Tel Aviv-Based AI TEM

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Facebook Creates New Tel Aviv-Based AI TEM

Apparently, Facebook is of the opinion that what it needs is more artificial intelligence (AI). The social media giant just announced the formation of the Data.AI group, a Tel Aviv-based AI team that will focus on machine learning, and developing tools to assist with deeper analytical insights.


Featured story. Artificial Intelligence, part IV.


According to a recent report, AI is a major source of growth for the Israeli tech industry.

Israel is home to over 1,000 companies, academic research centers, and multinational R&D centers specializing in AI, including those that develop core AI technologies, as well as those that utilize AI technologies for their vertical-related products such as in healthcare, cybersecurity, automotive, and manufacturing among others, according to the Start-Up Nation Central report.

Israeli companies specializing in artificial intelligence raised nearly 40 percent of the total venture capital funds raised by the Israeli tech ecosystem for 2018, despite accounting for just 17 percent of the total number of technology companies in the country, the report noted.

The SNC report noted that a number of events in 2018 boosted the AI ecosystem in Israel, including the launch of a new Center for Artificial Intelligence by Intel and the Technion-Israel Institute of Technology, and the announcement by US tech giant Nvidia (which acquired Israel’s Mellanox Technologies last month for $6.9 billion) that it too was opening a new AI research center.

A number of high-profile AI products developed by Israeli teams working for multinationals were also unveiled this year. In May, Google came out with Google Duplex, a system for conducting natural sounding conversations developed by Yaniv Leviathan, principal engineer, and Yossi Matias, vice president of engineering and the managing director of Google’s R&D Center in Tel Aviv. And in July 2018, IBM unveiled Project Debater, a system powered by artificial intelligence (AI) that can debate humans, developed over six years in IBM’s Haifa research division in Israel.

Earlier this year, the Israel Innovation Authority (IIA) warned that despite industry achievements, Israel was lagging behind other countries regarding investment in AI infrastructures and urgently needed a national AI strategy to keep its edge. The IIA called for the consolidation of all sectors – government, academia, and industry – to establish a vision and a strategy on AI for the Israeli economy.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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Using Artificial Intelligence on Facial Recognition Detects Rare Genetic Disorders

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Using Artificial Intelligence on Facial Recognition  Detects Rare Genetic Disorders

A new technological breakthrough is using AI and facial analysis to make it easier to diagnose genetic disorders. DeepGestalt is a deep learning technology created by a team of Israeli and American researchers and computer scientists for the FDNA company based in Boston. The company specializes in building AI-based, next-generation phenotyping (NGP) technologies to “capture, structure and analyze complex human physiological data to produce actionable genomic insights.”


Portions of this article were originally reported in NoCamels.com


DeepGestalt uses novel facial analysis to study photographs of faces and help doctors narrow down the possibilities. While some genetic disorders are easy to diagnose based on facial features, with over 7,000 distinct rare diseases affecting some 350 million people globally, according to the World Health Organization, it can also take years – and dozens of doctor’s appointments – to identify a syndrome.

“With today’s workflow, it can mean about six years for a diagnosis. If you have data in the first year, you can improve a child’s life tremendously. It is very frustrating for a family not to know the diagnosis,” Yaron Gurovich, Chief Technology Officer at FDNA and an Israeli expert in computer vision, tells NoCamels. “Even if you don’t have a cure, to know what to expect, to know what you’re dealing with helps you manage tomorrow.”

DeepGestalt — a combination of the words ‘deep’ for deep learning and the German word ‘gestalt’ which is a pattern of physical phenomena — is a novel facial analysis framework that highlights the facial phenotypes of hundreds of diseases and genetic variations.

According to the Rare Disease Day organization, 1 in 20 people will live with a rare disease at some point in their life. And while this number is high, there is no cure for the majority of rare diseases and many go undiagnosed.

“For years, we’ve relied solely on the ability of medical professionals to identify genetically linked disease. We’ve finally reached a reality where this work can be augmented by AI, and we’re on track to continue developing leading AI frameworks using clinical notes, medical images, and video and voice recordings to further enhance phenotyping in the years to come,” Dekel Gelbman, CEO of FDNA, said in a statement.

DeepGestalt’s neural network is trained on a dataset of over 150,000 patients, curated through Face2Gene, a community-driven phenotyping platform. The researchers trained DeepGestalt on 17,000 images and watched as it correctly labeled more than 200 genetic syndromes.

In another test, the artificial intelligence technology sifted through another 502 photographs to identify potential genetic disorders.

DeepGestalt provided the correct answer 91 percent of the time.


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Indeed, FDNA, a leader in artificial intelligence and precision medicine, in collaboration with a team of scientists and researchers, published a milestone study earlier this year, entitled “Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning” in the peer-reviewed journal Nature Medicine.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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From ‘Content is King’ to GODLIKE, Part 6

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From ‘Content is King’ to GODLIKE, Part 6

In part 5 of “From ‘Content is King’ to Godlike,” we looked at methods and metric used today to predict future outcomes based on contextual control of the environment. In this segment, we look at how Behavioral Economics is impacting rtificial intelligence (AI) algorithms, machine learning, and inferential logic.

What is Behavioral Economics?

Behavioral Economics is the study of human choice and decision-making. Unlike its classical economics predecessor, Behavioral Economics has two key divergences:

  • Whereas classical economic theory assumes that both markets and consumers are rational actors, Behavioral Economics does not. In fact, BE practitioners demonstrate the irony of predictability in human’s behaving badly, or irrationally. 
  • Building from that point, BE has developed a series of scientific methods to test, measure, and yes, predict human irrationality.
  • As the name somewhat suggests, BE bridges the academic disciplines of economics and psychology as well as other social sciences, neuroscience and even mathematics. That’s quite a lot.
  • Incorporates Heuristics, i.e., humans make 95% of their decisions using mental shortcuts or rules of thumb.
  • Incorporates Framing: The collection of anecdotes and stereotypes that make up the mental filters individuals rely on to understand and respond to events.
  • Addresses market inefficiencies that classical economics does not, including mis-pricing and non-rational decision making.

Behavioral Economics is a relatively new academic discipline whose roots go back to the 1960s largely from cognitive psychology. To many, Richard Thaler (Nobel Prize: 2017) is considered the father of BE. The University of Chicago professor has written six books on the subject.

Behavioral Economics Applications

While still considered a fledgling discipline, BE is growing rapidly, and arguably the most consequential new study area in academia. BE has already has been put to practical use in Marketing, Public Policy, and of course, AI, although its presence is rather opaque.

  1. PLAYING SPORTS
    Principle: Hot-Hand Fallacy—the belief that a person who experiences success with a random event has a greater probability of further success in additional attempts.

Example: In basketball, when players are making shot after shot and feel like they have a “hot hand” and can’t miss.

Relation to BE: Human perception and judgment can be clouded by false signals. There is no “hot hand”—it’s just randomness and luck.

2. TAKING AN EXAM
Principle: Self-handicapping—a cognitive strategy where people avoid effort to prevent damage to self-esteem.

Example: In case she does poorly, a student tells her friends she barely reviewed for an exam even though she studied a lot.

Relation to BE: People put obstacles in their own paths (and make it harder for themselves) in order to manage future explanations for why they succeed or fail.

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3. GRABBING COFFEE
Principle: Anchoring—the process of planting a thought in a person’s mind that will later influence this person’s actions.

Example: Starbucks differentiated itself from Dunkin’ Donuts through their unique store ambiance and product names. This allowed the company to break the anchor of Dunkin’ prices and charge more.

Relation to BE: You can always expect a grande Starbucks hot coffee ($2.10) to cost more than a medium one from Dunkin ($1.89). Loyal Starbucks consumers are conditioned, and willing, to pay more even though the coffee is more or less the same

4. PLAYING SLOTS
Principle: Gambler’s Conceit—an erroneous belief that someone can stop a risky action while still engaging in it.

Example: When a gambler says “I can stop the game when I win” or “I can quit when I want to” at the roulette table or slot machine but doesn’t stop.

Relation to BE: Players are incentivized to keep playing while winning to continue their streak, and to keep playing while losing so they can win back money. The gambler continues to perform risky behavior against what is in this person’s best interest.

5. TAKING WORK SUPPLIES
Principle: Rationalized Cheating—when individuals rationalize cheating so they do not think of themselves as cheaters or as bad people.

Example: A person is more likely to take pencils or a stapler home from work than the equivalent amount of money in cash.

Relation to BE: People rationalize their behavior by framing it as doing something (in this case, taking) rather than stealing. The willingness to cheat increases as people gain psychological distance from their actions.

These behavioral economics principles have major consequences on how we live our lives. By understanding the impact they have on our behavior, we can actively work to shape our own realities. As such, it’s becoming increasingly difficult, if not impossible, to decouple BE from AI, et al.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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From ‘Content is King’ to GODLIKE, Part 5

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From ‘Content is King’ to GODLIKE, Part 5

In part 4 of “From ‘Content is King’ to Godlike“, we exposed how determinism, or at least, deterministic factors and tendencies expose free will for the myth it really is, or at least in the context of its colloquial usage. In this segment, we’ll look at the futuristic applications of artificial intelligence (AI) and the many tech antecedents, including the many algorithm applications that inform AI that makes this probable. But before we proceed, it’s worth reiterating what algorithms are designed to do:

  • Solves problems in the most efficient way, that is, a way to accomplish a complex task quicker than any other way.
  • A way to analyze data in a way that provides a degree of certainty or predictable outcomes. Note these predictions are not absolute but rather, probabilistic.
  • A way to reason through a variety of data points toward the goal of sense-making.

Tackling the 99 percent of content that’s just sitting there.

Inferential search engines like Google are already quite capable of quickly indexing almost infinite quantities of content and data. But it’s also worth noting that what’s actually taking place in this indexing process is a variety of sorting algorithms “under the hood” that manifest in providing “on-the-fly” search results that answer the question, i.e., our search with high precision.

From there, the future of AI is really a plethora of algorithms stacked upon each other, and at other times, complex hierarchies that are invoked by the determinations made by other algorithms appearing further upstream.

Another way of looking at this, is not by algorithms per se, though they are, but rather, by metadata, and lots of it. It is the metadata and its juxtaposition with multi-algorithms that results is bizarre predictability.  Some examples:

  1. You have been divorced for 2 to 3 years from your wife, and find yourself buying Bud Lite at the grocery store, probably not realizing that your purchase was prompted by AI ads for that very product. Welcome to the world of semi-spooky correlation.
  2. You purchased a car, not online, and find yourself buying a smartphone dashboard holder from an unsolicited email. Did marketers know that you bought a car, and its year in order to determine your need for said product? Answer: Indeed they did.
  3. Your 54 years old and should be at a stage in your life where you are preparing for retirement. But you are being served up ads to go back to school for a graduate degree? Why on earth would that be happening? Answer: Based on your online behavior, these handy algorithms, et al. have determined — there’s that word again — it is probable that you are in the market to go back to school, and here’s the kicker, even before you knew it.

And only more of this is in store for all our futures. Question is, is this a good thing or something much more sinister, which will be the topic of our next post.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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From ‘Content is King’ to GODLIKE, Part 4

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From ‘Content is King’ to GODLIKE, Part 4

In “From Content is King’ to GODLIKE, Part 3,” we described the three important manifestations in which artificial intelligence (AI) manifests, and how you are already consuming and making decisions based on AI even though you probably are not aware of Big Tech Brother. In part 4, we look at this godlike creature as an omniscient force. And much like many Abrahamic gods which invoke optimal power, knowledge, and foresight, we examine implications on human free will and whether there is such a thing.

Free Will

For most, free will represents our capacity to behave autonomously within the reality we co-occupy with everyone else. (Well, at least most of us. Some people do not share our reality. They attest to seeing and hearing things that cannot be verified by anyone else. We call these things delusions. In addition, some people are seemingly incapable of showing empathy toward others. Mental health professionals designate such types as psychopaths or sociopaths.)

Many aspects of free will are strongly contested by philosophers, psychologists, and other academicians. But for our purposes, we’ll examine the ways in which content is presented to us on various digital devices such as computers, smartphones, and notebooks.

Example Number One: You are asked to come up with a name of any city of your choosing. You end up picking Philadelphia, but why did you choose Philadelphia of all the cities in your memory? What criteria did you use, or was the process random, or may be arbitrary? If recent studies are to be believed, you probably picked Philadelphia for arbitrary reasons along with a healthy helping of determinism. First, you probably are aware of several thousand cities, tens of thousands actually. But did you audition all of them when asked to pick a city? Nope. That literally takes way too much memory to pull off. So just like a computer uses RAM (rapid access memory) so does your brain. That leaves maybe a dozen or so cities that are in your own RAM, mostly cities you interact with regularly, or cities referenced very recently while consuming online (or offline) content. And now that you think about it, you know why you picked Philadelphia. You watched a Youtube video of Live Aid last night to see if the Led Zeppelin performance was as mediocre as your friend claims. Turns out, he was right.

It also turns out, based on your own account, that your selection of Philadelphia was determined by the factors you described. True. Not 100 percent deterministic but a probable choice indeed, one that becomes quite predictable, although not exact, among say, 20,000 of the most popular cities.

AI marketers know this too. So the next time you go to your favorite portal, take a look at those ads. Each one is contending for selection of your next so-called free choice.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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From ‘Content is King’ to GODLIKE, Part 3

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From ‘Content is King’ to GODLIKE, Part 3

In part 2 of Content is King’ to GODLIKE, we set the table for the showdown of a new godlike figure derived from harnessing content and apply artificial intelligence to it. We also drew distinctions between traditional, personal revelation gods and their contemporary contenders. In part 3, we look at how tech can become godlike by inculcating many godlike characteristics from heretofore unworked information, and how that information, once worked, can become autonomous knowledge. And Voila! we are encroaching on the purview of a virtual deity.

To fully grasp this ascendance we must implicate some mathematics, in particular, the many algorithms that drive AI.

What are algorithms anyway? Algorithms are automated rules that aspire to one of three things, and sometimes more than just one:

  • Solves problems in the most efficient way, that is, a way to accomplish a complex task quicker than any other way.
  • A way to analyze data in a way that provides a degree of certainty or predictable outcomes. Note these predictions are not absolute but rather, probabilistic.
  • A way to reason through a variety of data points toward the goal of sense-making.

Chances are when you do a Google search, you are engaging in at least goal 1, if not more than that. And when you go to a news portal like Cnn.com, those ads you see are being fed by algorithms that analyze your content consumption behavior which is why said ads are rarely irrelevant to you, even if you don’t click on them. Taking it one step further, you will notice ads that are not related to content you viewed recently (somewhere else) but are still very relevant. How can that be? Algorithms can correlate in ways that appear almost magical.

Algorithm definition (Wikipedia): a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.

In the next article we’ll take a look at real-world applications of the nascent godlike technology being used “on you” right now.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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From ‘Content is King’ to GODLIKE, Part 2

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From ‘Content is King’ to GODLIKE, Part 2

In part one of, “From ‘Content is King’ to GODLIKE“, we introduced some mind-boggling facts: namely that:

  • 90 percent of all content ever generated in the history of mankind, was created in the last two years
  • 99 percent of all content is yet to be accessed, let alone worked, mined or analyzed.

Throw artificial intelligence (AI) in the mix and it quickly becomes apparent that we are embarking on the creation of a technology that would rival any god ever envisioned — and there have been thousands. The big difference between the gods of the past and those of the present are worth elucidating.

Traditional or conventional gods provided answers to most if not all of life’s mysteries. Of course, there are problems with that, most notably, a lack of empirical rigors to go along with the robustness of the claims. Newer gods — I’ll use Google’s search engine as an example — are empirical for sure, and use information, i.e., empirical evidence, as an epistemic foundation. From there the commonalities between the old and new intersect again since both models are pretty big on predictions (or prophecy). And before I can complete that sentence we experience yet another bifurcation with the old depending on one or another form or revealed truth and the Google god relying on inference, induction, and most recently, artificial intelligence to answer the secular prayer more commonly known simply as THE SEARCH.

God or godlike dichotomies aside, what’s a civilization to do with all this content, especially since 99 percent of it is just sitting here and there (and everywhere), doing nothing? Well, we already have the technology, i.e., Google and similar technology, to harness it. That’s one thing but taking benign predictability — the search — to profound prophecy and beyond through weird and counterintuitive correlations that provide answers even before we think of them, let alone type them into Google, is where the future of content vis-a-vis AI is heading.

The next article will get into the specifics of precisely how this might look, using everyday problems and contemplations.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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How the Death of ‘Content is King’ May Take On Godlike Proportions: Part 1

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How the Death of ‘Content is King’ May Take On Godlike Proportions: Part 1

To say I was “blown away” by a recent editorial in NoCamels.com by Yaniv Garty, General Manager of Intel Israel, is a frustatingly cliche due to the poverty of English usage as it exists today. And it wasn’t Garty’s predictions of what the world could look like by 2025 that captured and downright agitated my imagination (in a way I enjoyed). Sure, his IT prophecies are all plausible among numerous pundits, evangelists, and visionaries. Nope. It wasn’t that.

It was the data, specifically the vast quantities of data being generated, even right now. Consider these three incredible facts:

  1. Of all the data created since the beginning of civilization, 90 percent of it has been generated in the last 2 years.
  2. By 2025, total data will reach 163 zettabytes. You probably never heard of a zettabyte, and you may want to pause before you attempt to digest it. 163 zettabytes is 1,000 Billion terabytes. Even with the comparison, I still find it incomprehensible.
  3. Only 1 percent of all data has been accessed in any meaningful way.

Garty, who is charged with growing Intel’s hardware for IT ecosystem of the future, has a lot to think about, namely…

Artificial Intelligence (AI), and how it can begin to mine the 99 percent for, among other things, greater insights and predictive measures. Intel already has its eyes on the medical field with aspirations to provide tailor-made solutions for each patient, perhaps and beyond, like unique biological and genetic characteristics.


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Another good example is the interface between data and transportation: The potential of saving lives by lowering the number of accidents made possible with autonomous driving is incredible. But to reduce accidents we need a combination of technologies working together – from computer vision to end-computing, mapping, cloud, and of course AI. All these, in turn, require a systematic change in the way the industry views data-focused computing and technology.

My personal take is that the IT ecosystem of the future will more and more resemble the different executive and subordinate functions of the human brain with neuroscientists and computer scientists conspiring to construct the greatest monster even seen: one giant decentralized and interdependent mega-brain.

In the next segment of this series, we will consider the moral and religious implications of this almost godlike monstrosity.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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Fast-Tracking Pharmaceutical Development with Artificial Intelligence

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Fast-Tracking Pharmaceutical Development with Artificial Intelligence

Pharmaceutical R&D has huge barriers to entry. The cost is just too expensive for ambitious startups to even consider, on average about $2.5 billion just to bring a new drug to market. And there’s also a public interest cost which is seldom discussed. Namely, the cynical fact that drugs that can’t make much money are never developed. Hopefully, both of these impediments will lessen in the near future. Once again, artificial intelligence (AI) and machine learning (ML) are at the forefront in the drive reduce the time and cost to develop new drugs. How? By letting the molecules do the talking.


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The following article was originally published in NoCamels.com

The cost of developing a new pharmaceutical drug, from the research and development stage to market approval, runs at about $2.6 billion, according to a 2014 report published by the Tufts Center for the Study of Drug Development (CSDD) cited by the Scientific American. It also takes between 10 to 15 years.

Israeli scientists say they have developed a revolutionary smart method to discover and develop new drugs, based on artificial intelligence and machine learning, that will dramatically shorten preparation time and reduce costs.Dr. Kira Radinsky, a renown data scientist and a visiting professor at the Technion – Israel Institute of Technology, and Shahar Harel, a PhD student at the university’s computer science department, presented their system late last month at the KDD 2018 conference in London, an annual event on Big Data and Machine Learning that draws prominent world academics and industry leaders.

Radinsky and Harel’s system seeks to tap into the modern-day, computerized processes of screening and selecting molecules with the greatest therapeutic potential – of which there are more than stars in the galaxy, making this an enormous task.

Their working hypothesis is that drug development “vocabulary” is similar to that of a natural language.

Harel said in a university statement that the system he and Radinsky developed, founded on artificial intelligence (AI) and deep learning, “acquired this language based on hundreds of thousands of molecules.”

“We are essentially presenting here an algorithm which addresses the creative stage of drug development – the molecule discovery stage,” said Harel. “This capacity leans upon our mathematical innovation, which enables the computer to understand the chemical language and to generate new molecules based upon a prototype.”

The researchers instructed the system to propose 1000 drugs based upon old drugs and were surprised to discover that 35 of the new drugs generated by the system are existing, FDA-approved drugs developed and approved after 1950. Radinsky said in a statement that the system “is not only a means of streamlining existing methods but also entirely new drug development and scientific practice paradigms.”

“Instead of seeking out specific correlations based upon hypotheses we formulate, we allow the computer to identify these connections from within a massive sample size, without guidance. The computer is not smarter than man, but it can cope with huge amounts of data and find unexpected correlations,” she added.

Radinsky indicated that a similar computerized process is how, in another study, the scientists managed to find the unknown side effects of various drugs and drug combinations.

“This is a novel type of science which is not built upon hypotheses tested in an experiment, rather, upon data that generated the research hypothesis,” she said.

The Technion said in a statement that the breakthrough is particularly significant in light of Eroom’s Law, which asserts that the number of new drugs approved by the FDA should decline at a rate of approximately 50 percent every nine years. The term was coined in 2012 in an article published in Nature Reviews Drug Discovery and is a reverse order of Moore, the name of Gordon Moore, one of the founders of Intel. Moore observed that the number of transistors in a dense integrated circuit doubles every two years. In contrast, Eroom’s Law notes that each year, fewer and fewer drugs are marketed.

Dr. Radinksy projects that “this new development will accelerate and reduce costs of development of new and effective drugs, thereby shortening the time patients will have to wait for the drugs. In addition, this breakthrough is expected to lead to the development of drugs that would not have been generated with the conventional pharmacological paradigm.”

The system is currently being deployed for use in collaboration with pharmaceutical companies to further analyze the additional generated molecules, the scientists said.

Diane Israel is a Chicago native and long-time supporter and advocate of the American Israel Public Affairs Committee (AIPAC). She is also famous for her culinary recipes. Diane can be reached at Diane@IsraelOnIsrael.com

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