Archive For The “Artificial Intelligence” Category

From ‘Content is King’ to GODLIKE, Part 3

By |

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, 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.

Read more »

From ‘Content is King’ to GODLIKE, Part 4

By |

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.

Read more »

From ‘Content is King’ to GODLIKE, Part 5

By |

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.

Read more »

Fast-Tracking Pharmaceutical Development with Artificial Intelligence

By |

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.

See previous articles on artificial intelligence by Diane Israel. More on Diane Israel.

The following article was originally published in

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.

Read more »

The Precarious Union of Artificial Intelligence And Blockchain

By |

The Precarious Union of Artificial Intelligence And Blockchain

I have written extensively about artificial intelligence (AI), noting its far-reaching tentacles, diverse applications, and ubiquities. But there’s a companion platform that has been raging for a few years now, and that platform is Blockchain.

Unless you’re a tech geek, you probably have a cursory at best understanding, having heard of it in news reports. The best way to put Blockchain into context is to understand its most popular application: Bitcoin. This cryptocurrency has caused volatile swings in the financial markets, has caused the biggest banks and lending institutions to take notice, even throwing millions in R&D to bone up on the technology.

More on Diane Israel.

What’s important to understand is the relationship between Bitcoin (and other lesser known cryptocurrencies) and Blockchain. Bitcoin runs on blockchain and currently needs blockchain to function.

As such, the marriage between AI and Blockchain is a natural one, although not necessary. The appeal, however, is the security features that blockchain promises, and the egalitarian way in which information is stored and distributed.

The Israeli Connection

Israeli startups are at the forefront of both of these sectors, receiving notable attention and investments from global players which are further propelling Israeli development in these industries.

As recently reported in

In Israel, the average investment per deal in AI grew five times in value, from $2 million in 2016 to $10.2 million in 2017. Subsequently, the growth in this sector is reflected in the overall investment numbers for AI in Israel, with the market growing from $55 million in 2016 to $472 million in 2017, according to the Geektime Annual Report 2017: Startups and venture capital in Israel, published in January.

A major setback.

For a few years now, financial institutions have been experimenting with Blockchain for its “very strong” security features. Not so fast, however. What was lost by most was FPI special investigator Robert Mueller’s recent charges filed against senior Russian military leaders, many of whom take their orders directly from Vladimir Putin. What the filing showed, in great detail, is how Mueller’s team was able to deconstruct the way in which the Russians used Bitcoin to pay for everything from website hosting, domain registration, and even paying for Facebook ads. According to many Bitcoin experts, the advantage of Bitcoin/Blockchain is its ability to remain anonymous throughout the transaction process. And while we still don’t know how Mueller’s team was able to attribute a Bitcoin account directly to its true owner is proof positive that the technology is vulnerable.

Netting it out.

What this means is simple. Blockchain may have some appealing features for the marketplace, but security isn’t one of them. As such, the fledgling marriage between AI and Blockchain may be short-lived and was never a necessary one.

Read more »

Tech Emergence And Convergence Demystified

By |

Tech Emergence And Convergence Demystified

While at times the evolution of technological innovation may seem chaotic with no clear purpose, goal or objective — many new technologies seem to come out of nowhere — there is an unseen hand at play. Adam Smith’s Wealth of Nations foretells of this somewhat mystical phenomenon whereby markets have their own agency, filling in market gaps as if a transcendental being was overseeing our economy. What Smith and many others who followed him neglected to notice, whether intentionally or not, is that real people with keen awareness of present conditions coupled with future need are these mysterious beings. In other words, we have discovered this unseen hand, and it’s us!

I’ll use applications that just about everyone is aware of to demonstrate who all of this works. It’s a bit of an oversimplification but appropriate for this demonstration.

  1. Microsoft Word released as a standalone application
  2. Microsoft Excel released as a standalone application
  3. Microsoft PowerPoint released as a standalone application

Then Microsoft Office released comprising all three with true integration that made interfacing with all three rather easy. In other words, innovations begin as separate entities but eventually and naturally consolidate into one integrated application.

I mention this because we are witnessing the same sort of stovepipe development and consolidation happening right now. However, this phenomenon is no longer constrained to applications but rather more vague concepts such as content and speed.

On the content side, artificial intelligence (AI) is the driving force. Note that there is real category confusion about AI that is to be expected. Legacy labels such as neural networks and machine learning are becoming meaningless because each overlap and can rightly be called AI, which depicts a consolidation of applications in real-time. Now couple that with what AI needs to allow for greater capabilities, ones that science fiction describes, and there we have it. Speed. And this increased speed, actually 20to 50 times faster than its predecessor, is coming through 5g wireless.

Long story short, if you want to see the future of tech innovation, keep your eyes on AI and 5G. Throw in the peripheral technologies of the Internet of Things (IoT), and the picture becomes clear.

Read more »

IBM’s AI Legacy Continues With PROJECT DEBATER

By |

IBM’s AI Legacy Continues With PROJECT DEBATER

IBM may have missed the mark on becoming a PC giant after its poorly calculated entry into the market a few decades ago. The computer-before-there-was-a-computer market giant also was a bust with ill-fated entries into PC operating systems, OS2 RIP! And quite frankly more of the same from its entrants into Web servers, e-commerce, content management…, it’s a very long list of failures.

But for artificial intelligence (AI), IBM has found its niche, which itself is ironic since a computer giant is not supposed to be a niche player. From the early days of Deep Blue, the first computer to beat reigning world chess champion Gary Kasparov back in 1996.

Since then IBM’s AI has gotten a lot smarter, and with the help of its Israeli IBM Haifa division, it’s debating humans in situations for which the rules are not nearly as structured as Chess as the following article excerpt from explains.

Dubbed PROJECT DEBATER, it was developed over six years in IBM’s Haifa research division in Israel.

At the unveiling two weeks ago in San Francisco, the system engaged in its first-ever live, public debate. Its opponents were two Israeli debate champions. Israel’s 2016 debate champion Noa Ovadia took on the system for a discussion on whether space exploration should be subsidized by the government. Dan Zafrir, a professional debater, argued Project Debater on the value of telemedicine and whether it should be used more widely.

Each side delivered a four-minute opening statement, a four-minute rebuttal, and a two-minute summary, according to a June 18 post by IBM Research Director Arvind Krishna

The humans were said to have won, but by a close call. According to an audience survey cited by Krishna in an interview with Fox News, the computer lacked the persuasive speaking nuances of the debate champs but possessed more knowledge on the topics. Krishna wrote that IBM “selected from a curated list of topics to ensure a meaningful debate. But Project Debater was never trained on the topics.”

This week, Project Debater performed once again against two human debaters, this time in Israel where the team behind the project proudly displayed it.

At the event at IBM’s Givatayim offices held for local press, the system this time challenged Israeli professional debaters Yaar Bach and Hayah Goldlist-Eichler on mass surveillance methods, and genetic engineering, respectively.

IBM’s Israel CEO and country manager Daniel Melka told the audience that the company developed “very special technology” that is “a significant milestone in the development of Artificial Intelligence technology,” according to the Times of Israel.

In a video presentation ahead of the unveiling, Noam Slonim, the principal investigator of Project Debater and senior technical staff member (STSM) at the IBM Haifa Research Lab, said the goal of the project was “to demonstrate that we can have a meaningful and viable discussion between men and machine.”

Project Debater, Krishna wrote, “moves us a big step closer to one of the great boundaries in AI: mastering language. It is the latest in a long line of major AI innovations at IBM, which also include “Deep Blue,” the IBM system that took on chess world champion Garry Kasparov in 1997, and IBM Watson, which beat the top human champions on Jeopardy! in 2011.”

IBM’s recent developments in machine thinking surpass that of existing products such as Apple’s Siri and Amazon’s Alexa. These devices primarily recite rote information, whereas Project Debater uses facts to reason and construct arguments on topics that have no right or wrong answers. According to IBM, the technology accomplishes this through first recognizing opponents’ arguments through Watson Speech to Text. Then, it identifies relevant expressions in its database of hundreds of millions of articles from trusted journals and magazines. Lastly, it eliminates redundancies, prioritizes arguments and composes coherent English speech.

“Subsidizing space exploration is like investing in really good tires,” Project Debater rebutted Ovadia in the government-sponsored space research debate in San Francisco. “It may not be fun to spend the extra money, but ultimately you know both you and everyone else on the road will be better off.” It further argued that space exploration also inspires the younger generation to pursue careers in science and technology.

The computer also attempted to make jokes during the debate. “You are speaking at the extremely fast rate of 218 words per minute. There is no need to hurry,” Project Debater told Ovadia.

Up against Zafrir in the telemedicine debate, the system admonished its opponent saying: “Fighting technology means fighting human ingenuity.” And in the debate this week against Goldlist-Eichler, who, for the sake of argument expressed her suspicions of the safety of technological advancement, Project Debater said: “I can’t say this is getting on my nerves, because I don’t have any.”

The project is being hailed as the onset of a new era for human-machine interaction. Krishna says IBM’s mission was to develop broad AI that learns across different disciplines to augment human intelligence.

And Krishna said Project Debater could become “the ultimate fact-based sounding board without the bias that often comes from humans.”

The limitations

Project Debater has its limitations. The system is currently programmed to follow a strict 20-minute debate format for 100 topics, according to The New York Times.

Furthermore, Wired magazine reported that Project Debater sometimes failed to address certain points and to construct rebuttals in line with the opponents, and provide real-life context for its arguments.

Krishna acknowledged that building the system was a “remarkably difficult and complex challenge,” and that it makes mistakes, “just like people.”

Though the Israeli team built Project Debater with three ground-breaking AI capabilities (data-driven speech writing and delivery, listening comprehension that can identify key claims hidden within long continuous spoken language, and modeling human dilemmas in a unique knowledge graph to enable principled arguments), the system must still learn to “adapt to human rationale and propose lines of argument that people can follow.”

“Debate rules stem from a human culture of discussion and are not arbitrary, and the value of arguments is often inherently subjective…In debate, AI must learn to navigate our messy, unstructured human world as it is – not by using a pre-defined set of rules, as in a board game,” he wrote.

While PROJECT DEBATER technology lost the debate, it demonstrated more knowledge than its two human counterparts. So what caused PD to lose? Influence and persuasion. PD simply lacked the subtleties of language and nuanced delivery to maximize its influence. In other words, it lost on style points, which tells us a lot. Indeed, style does matter when it comes to persuading others to accept the knowledge being presented.

Read more »