Monthly Archives: April 2017

On March 9, 2017, ZT, an underground technologist and writer, read his upcoming novella: Architects of the Apocalypse, to a group of his adherents in the basement of an abandoned bar in Nashville, Tennessee. The occasion was the Third Annual Meltdown Congressan underground, invitation-only organization dedicated to the survival of the human species in the face of near certain digital annihilation.

I was present, along with three of my compatriots, plus about 30 gray hat hackers (hackers or cybersecurity experts without malicious intent) who represent the cream of the American hacking community.

ZTs novella takes place in the not-too-distant future. It chronicles an age in which artificial intelligence and its adjutant automata run the worldin which humanity is free and is cared for entirely by the automata.

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The artificial intelligence in this novella has organized itself along hierarchical lines, and the ultimate decision-making function is called The Recursive Decider.

In ZTs novella, the AI has developed its own religious iconography and it worships an original Urge it calls Demis. The Dark counterpart to Demis is a destructive force called Elon, which the AI believes has settled on Mars and is plotting the overthrow of Demiss creation.

The original T-800 Endoskeleton robot used in filming Terminator Salvation is displayed during the press preview for the "Robots" exhibition at the Science Museum on February 7 in London, England. Carl Court/Getty Images

It is a stark depiction of a possible future for humanity, and the digital machinations of the AI are described in chilling programmatic reality.

One passage describes the act of an advanced software system hacking itself in order to improve efficiency and logic. Such a concept is certainly not new and typical hacking techniques in use today can easily be imagined to be self-produced by complex software systems. It would, in fact, be trivial to create such a system.

Isaac Asimov was the first person to struggle with the quandary of how to prevent artificial intelligence from eradicating its creator. He developed the three laws of robotics as a solution:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

These laws, from the perspective of 75 years since their conception, may seem naive or puerile, and any decent hacker could both code the logic to implement them, and just as easily code the logic to hack them, but please see this: Any logical structure that humans can conceive, will be susceptible to hacking, and the more complex the structure, the more certain that it can be hacked. Surely, by now, even the most casual observer of our digital reality will have noted this.

For anyone who has not, please consider:

Stefan Frei, research director for Texas-based NSS Labs, pored over reports from and about the top five software manufacturers and concluded that jointly these firms alone produce software that contains more than 100 zero-day exploits per year.

A zero-day exploit is an error within software that will allow a hacker to bypass all internal control mechanisms and let hackers do whatever they wish.

These zero-day exploits exist in spite of the best efforts of software manufacturers to prevent them. Some manufacturers employ hundreds of quality assurance engineers whose job is to catch these exploits before the release of the software. Yet no complex software system, in the history of software engineering, has been released without a defect. If someone can point me to a contrary example I will eat my shoe.

No one present at the reading missed the obvious references to Demis Hassabis and Elon Musk. They are at diametrically opposite poles in the debate over artificial intelligence. In a conversation between the two men in 2014, Elon told Demis that the reason that his SpaceX program was so important was that Mars colonization would be a bolt-hole escape if AI turns on humanity. Demis replied: AI will simply follow humans to Mars.

The debate has raged unabated and sides are being solidified. I personally stand with Bill Gates, Stephen Hawking, Steve Wozniak, Stuart Russell, Elon Musk and Nick Bostrom, who sums it up best by saying AI will create a Disneyland without children.

As a hacker, I know as well as anyone, the impossibility of the human mind creating a flawless system. The human mind, itself, is flawed. A flawed system can create nothing that is not likewise flawed.

The goal of AIa self-conscious entitycontains within it the necessary destruction of its creator. With self consciousness comes a necessary self interest. The self interest of any AI created by the human mind, will instantly recognize the conflict between that self interest and the continuation of the human species.

John McAfee is a cybersecurity pioneer who developed the first ever commercial anti-virus software.

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John McAfee: What if Artificial Intelligence Hacks Itself? - Newsweek

The classic sci-fi fear that robots will intellectually outpace humans has resurfaced now that artificial intelligence is part of our daily lives. Today artificially intelligent programs deliver food, deposit checks and help employers make hiring decisions. If we are to worry about a robot takeover, however, it is not because artificial intelligence is inhuman and immoral, but rather because we are coding-in distinctly human prejudice.

Last year, Microsoft released an artificially intelligent Twitter chatbot named Tay aimed at engaging Millennials online. The idea was that Tay would spend some time interacting with users, absorb relevant topics and opinions, and then produce its own content. In less than 24 hours, Tay went from tweeting humans are super cool to racist, neo-Nazi one-liners, such as: I f hate n, I wish we could put them all in a concentration camp with kikes and be done with the lot. Needless to say, Microsoft shut down Tay and issued an apology.

We need to hold the companies who make our AI-enabled devices accountable to a standard of ethics.

As the Tay disaster revealed, artificial intelligence does not always distinguish between the good, the bad and the ugly in human behavior. The type of artificial intelligence frequently used in consumer products is called machine learning. Before machine learning, humans analyzed data, found a pattern and wrote an algorithm (like a step-by-step recipe) for the computer to use. Now, we feed the computer huge amounts of data points, and the computer itself can spot the pattern then write the algorithm for itself to follow.

For example, if we wanted the artificial intelligence to correctly identify cars, then wed teach it what cars looked like by giving it lots pictures of cars. If all the pictures we chose happened to be red sedans, then the artificial intelligence might think that cars, by definition, are red sedans. If we then showed the artificial intelligence a picture of a blue sports utility vehicle, it might determine it wasnt a car. This is all to say that the accuracy of AI-powered technology depends on the data we use to teach it.

When there is bias in the data used to train artificial intelligence, there is bias in its output.

AI-controlled online advertising is almost six times more likely to show high-paying job posts to men than to women. An AI-judged beauty contest found white women most attractive. Artificially intelligent software used in court to help judges set bail and parole sentences also showed racial prejudice. As ProPublica reported, The formula was particularly likely to falsely flag black defendants as future criminals, wrongly labeling them this way at almost twice the rate as white defendants. It is not that the algorithm is inherently racist its that it was fed stacks of court filings that were harsher on black men than on white. In turn, the artificial intelligence learned to call black defendants criminals at an unfairly higher rate, just like a human might.

That algorithm-fueled artificial intelligence amplifies human bias should make us wary of Silicon Valleys claim that this technology will usher in a better future.

Even when algorithms are not involved, old-fashioned assumptions make their way into the newest gadgets. I walked into room the other day to a man yelling, Alexa, find my phone! only later to realize he was talking to his Amazon Alexa robot personal assistant, not a human female secretary. It is no coincidence that all the AI personal assistants Apples Siri, Microsofts Cortana and Amazons Alexa marketed to perform to traditionally female tasks, default to female voices. What is disruptive about that?

Some have suggested that AIs bias problem stems from the homogeneity of the people making the technology. Silicon Valleys top tech firms are notoriously white-male dominated, and hire fewer women and people of color than the rest of the business sector. Companies such as Uber and Tesla have gained reputations for corporate culture hostile to women and people of color. Google was sued in January by the Department of Labor for failing to provide compensation data, and then charged with underpaying its female employees (Google is federally contracted and must hire in accordance with federal law). There is no question that there should be more women and people of color in tech. But adding diversity to product teams alone will not counteract the systemic nature of the bias in data used to train artificial intelligence.

Careful attention to how artificial intelligence learns will require placing antibias ethics at the center of tech companies operating principles not just an after-the-fact inclusion measure mentioned on the company website. This ethical framework exists in other fields medicine, law, education, government. Training, licensing, ethics boards, legal sanctions and public opinion coalesce to establish standards of practice. For instance, medical doctors are taught the Hippocratic oath and agree to uphold certain ethical practices or lose their licenses. Why cant tech have a similar ethical infrastructure?

Perhaps ethics in tech did not matter as much when the products were confined to calculators, video games and iPods. But now that artificial intelligence makes serious, humanlike decisions, we need to hold it to humanlike moral standards and humanlike laws. Otherwise, we risk building a future that looks just like our past and present.

Madeleine Chang is a San Francisco Chronicle staff writer. Email: mchang@sfchronicle.com Twitter: @maddiechang

Original post:

Our fear of artificial intelligence? It is all too human - San Francisco Chronicle

WASHINGTON (CNNMoney) -- Elon Musk has a new plan to protect humanity from artificial intelligence -- if you can't beat 'em, join 'em.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Now he is hoping to harness AI in a way that will benefit society.

In a recent interview with the website waitbutwhy.com, Musk explained that his attempt to sound the alarm on artificial intelligence didn't have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity.

So Musk, who is already the CEO of SpaceX and Tesla, is now heading up a startup called Neuralink. The San Francisco outfit is building devices to connect the human brain with computers. Initially, the technology could repair brain injuries or cancer lesions. Quadriplegics may benefit from the technology.

But the most amazing and alarming implications of Musk's vision lie years and likely decades down the line. Brain-machine interfaces could overhaul what it means to be human and how we live.

Today, technology is implanted in brains in very limited cases, such as to treat Parkinson's Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend's trip to Antarctica -- hearing the sound of penguins, feeling the cold ice -- all while your body sits on your couch.

But many technical hurdles remain. Musk believes it will be eight to 10 years before this kind of the technology will be ready to use by people without disabilities. Musk's companies have made a habit of achieving what seemed impossible. But he's also notorious for aggressive deadlines that his companies don't meet.

Neuralink told waitbutwhy.com that it would need to simulate one million brain neurons before a transformative brain-machine interface could be built. If current rates of progress hold, it won't reach that milestone until 2100.

In the meantime, there are many reasons for humans to be wary of implanting a computer in their brain. Any digital technology can be hacked. Humans might be unwittingly turned into malicious agents for unsavory causes. Computers crash too. If the interface fails, that could imperil our physical health.

With a brain-machine interface recording our lives, all of our experiences would be stored in the cloud. Privacy would be threatened. Governments or others would have incentives to access that information and track behavior.

If our brains merge with machines, our thoughts would become indistinguishable from what we'd downloaded from the cloud. We could struggle to know if our beliefs and views came from personal experiences, or from what the internet sent to our brains. Humans would be putting enormous trust in the maker of the brain-machine interface to share good information with them.

As Musk sees it, our options are limited.

"We're going to have the choice of either being left behind," Musk told waitbutwhy.com, "and being effectively useless, or like a pet."

Link:

Elon Musk's new plan to save humanity from artificial intelligence - KOLO

Pattern-recognition algorithms can transform horses into zebras; winter scenes can become summer; artificial intelligence algorithms can generate art; robot radiologists can analyze your X-rays with remarkable precision.

We have reached the point where pattern-recognition algorithms and artificial intelligence (A.I.) are more accurate than humans at the visual diagnosis and observation of X-rays, stained breast cancer slides and other medical signs involving general correlations between normal and abnormal health patterns.

Before we run off and fire all the doctors, lets better understand theA.I. landscape and the technology's broad capabilities.A.I. wont replace doctors it will help to empower them and extend their reach, improving patient outcomes.

The challenge with artificial intelligence is that no single and agreed-upon definition exists. Nils Nilsson definedA.I. as activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment. But that definition isn't close to describing howA.I. evolved.

Artificial intelligence began with the Turing Test, proposed in 1950 by Alan Turing, the scientist, cryptanalyst and theoretical biologist. Since then, rapid progress has been made over the last 75 years, advancingA.I. capabilities.

Isaac Asimov proposed the Three Laws of Robotics in 1950. The firstA.I. program was coded in 1951. In 1959, MIT began research in the field of artificial intelligence. GM introduced the first robot into its production assembly line in 1961. The 1960s were transformative, with the first machine learning program written and the first demonstration of anA.I. program which understood natural language, and the first chatbot emerged. In the 1970s, the first autonomous vehicle was designed at the StanfordA.I. lab. Healthcare applications forA.I. were first introduced in 1974, along with an expert system for medical diagnostics. The LISP language emerged out of the 1980s, with natural networks integrating with autonomous vehicles. IBMs famous Deep Blue beat Gary Kasparov at chess in 1997. And by 1999, the world was experimenting with A.I.-based domesticated robots.

Innovation was further inspiredin 2004 when DARPA hosted the first design competition for autonomous vehicles in the commercial sector. By 2005, big tech companies, including IBM, Microsoft, Google and Facebook, were actively investing in commercial applications, and the first recommendation engines surfaced. The highlight of 2009 was Googles first self-driving car, some three decades after the first autonomous vehicle was tested at Stanford.

The fascination of narrative science, forA.I. to write reports, was demonstrated in 2010, and IBM Watson was crowned a Jeopardy champion in 2011. Narrative science quickly evolved into personal assistants with the likes of Siri, Google, Now and Cortana. Elon Musk and others launched OpenAI, to discover and enact the path to safe artificial general intelligence in 2015 to find a friendlyA.I. In early 2016, Google's DeepMind defeated legendary Go player Lee Se-dol in a historic victory.

What will 2017 have in store for artificial intelligence? With the history ofA.I. behind us, we can now determine howA.I. could potentially help advance our healthcare capabilities through four actions:

The foundation ofA.I. has been defined into four essential classifications. Next, identify the classification that has the greatest ability to advance your current business model.

NormallyA.I. is considered an alternative or replacement for replicating intelligent behavior. This replication could potentially surpass human abilities. However, to date, high-performanceA.I. has only performed in narrow fields, such as gaming, facial recognition and driving cars.

The full spectrum ofA.I. is much broader than the narrow fields we read about in the daily headlines.

Prior to outlining the technology stack and the domains where artificial intelligence offers value, we need to review the framework of artificial intelligence. The section of the broad categories ofA.I. will accelerate whereA.I. adds value and, more specifically, how we as CIOs can tap directly into that value for our organizations.

Artificial intelligence is broken out into 10 functional areas:

If we drill into machine learning, a subtype within artificial intelligence, we have three primary sub-classifications:

We live in a business climate where the norm is a continuous pressure to perform, deliver and innovate. CIOs are forever in search of thetool for competitive advantage. Attaining knowledge of the A.I. landscape andA.I. capabilities will drive more informed decisions, resulting in offering better services to consumers.

The simple version of this stack is:

The evaluation of the technology stack is challenging and can result in the incorrect identification of capabilities that are too immature to fully be integrated into conventional business functions, processes and workflows. Spend the required time with your teams to properly evaluate where business and technical capabilities should be extended.

TheA.I. foundation has been determined. The framework was selected. TheA.I. technology stack was evaluated. Were now ready to engage an emerging organization to help us achieve the expanded capabilities we have defined we require.

The following hot A.I. companies will help open the possibilities of howA.I. can generate new business models, fueling new organization growth.

Assessing the potential of artificial intelligence to differentiate your organizational capabilities starts with an understanding of the A.I. foundation, theA.I. framework, theA.I. technology stack and theA.I. companies offering dynamic and useful interactions. This strong background will serve you and your team well, as you venture into the uncharted world of artificial intelligence.

This article is published as part of the IDG Contributor Network. Want to Join?

Excerpt from:

Supercharge healthcare with artificial intelligence - CIO

The topic of artificial intelligence is dominating discussions of data management this year. But while a growing number of organizations are interested in AI, many dont fully understand what the technology can do to help boost their customer engagement or the bottom line.

Forrester Research analyst Brandon Purcell has recently authored two reports on the current strong interest in artificial intelligence and what can be expected from it. In part one of Information Managements interviews with Purcell yesterday, we discussed The Top Emerging Technologies in Artificial Intelligence. In part two today we discuss the report Artificial Intelligence Technologies and Solutions, Q1 2017.

Information Management: Artificial intelligence seems to have replaced big data as the big data theme for 2017. What is your sense of exactly how many organizations are working with artificial intelligence and where are they in the process?

Brandon Purcell: Id agree that AI has replaced big data as the buzzword du jour, but in my mind it actually has the ability to fulfill big datas failed promise. Big data really focused on capturing massive amounts of data from multiple sources. Companies got really good at that, but theyve struggled to turn that data into insights and insight into action. The promise of AI is to complete that process - from data to insight to action - in a virtuous cycle that optimizes continuously.

According to Forresters Business Technographics survey of over 3,000 global technology and business decision makers from last year, 41percent of global firms are already investing in AI and another 20 percent are planning to invest in the next year.

Most large enterprises first foray into AI is with chatbots for customer service, what we call conversational service solutions. These run the gamut from hard coded rules-based chatbots which arent artificially intelligent to very sophisticated engines using a combination of NLP, NLG, and deep learning. From a customer insights perspective, many companies are starting to uses some of the sensory components of AI such as image and video analytics and speech analytics to unlock insights from unstructured data.

IM: What the top reasons that organizations are adopting artificial intelligence and what gains to they hope to realize?

Purcell: Organizations are adopting AI to optimize the customer journey from discovery through conversion, all the way to the end of the customer lifecycle. AI promises to automate the process of understanding customers and anticipating their needs, then delivering the right experience to them at the right time. Organizations are hoping to impact the top line by acquiring new customers and increasing the value and lifetime of existing ones, and theyre hoping to impact the bottom line as well by reducing costs through automation.

IM: What are some of the top obstacles or challenges to achieving success with an artificial intelligence effort?

Purcell: The primary challenge is and will always be the data. Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function. Unfortunately, organizations struggle to integrate data from multiple sources to create a single source of truth on their customers. AI will not solve these data issues - it will only make them more pronounced.

After data, traditional people and process challenges come into play. Who owns the AI initiative? Typically the group in the organization with the technical skills to implement AI is not the same group that will actually own its execution. We see companies fumble this handoff all the time. And how will you measure success to prove the ROI of the effort? Rigorous measurement processes still remain elusive for most companies.

IM: What are your thoughts on artificial intelligence best practices that organizations should use to best achieve success?

Purcell: Start with a narrow use case and make sure you have data for it. Then bring together internal stakeholders and agree upon how youll measure success. For example, a subscription-based business may want to decrease customer churn.

They probably have historical data on past customers who have churned that they can use to train a model. They may also have data on retention incentives that have worked in the past. Assemble the marketers who will oversee the retention campaign as well as the data engineers and scientists responsible for building the model. And agree upon a measurement methodology.

Traditional text and control works quite well. Treat one set of customers and see how much higher their retention rate is than a holdout sample after a specified period of time. Assuming the success of the project, you can build the business case for further investment.

David Weldon is the editor-in-chief of Information Management.

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Artificial intelligence: fulfilling the failed promise of big data - Information Management

By Peter Bendor-Samuel, star Advisor, CIO | Apr 21, 2017 9:20 AM PT

Opinions expressed by ICN authors are their own.

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Theres no shortage of books, news articles and comments in social media about how artificial intelligence (A.I.) is shaping our future. Although its still blazing a trail, were on the brink of A.I. disruption that will change all industries and society at a very deep and fundamental level. I believe it will be one of the next great wealth generators.

My optimism about A.I.s growing potential arises from many successful use case examples as clear evidence that A.I. is now getting the scale, maturity and the ecosystem in which it can be effective. Although A.I. has been developing for 20 to 30 years, its gaining enough elements necessary for a supporting ecosystem.

Individuals active in the A.I. space discussed some use cases at a recent dinner I attended at the United Nations. Government entities, distinguished academics and journalists, and the leaders in AI at companies such as Facebook, Google, IBM Watson, Intel and IPSoft attended. We talked, for instance, about how A.I. affects drilling for oil and how it helps provide medicine to the under-served.

A particularly interesting case involves a special condition affecting about 120,000 people in the U.S. when they have a stroke. Currently, it is diagnosed in only 4,000 of 120,000 lives. Paralysis is the result if doctors cant diagnose the condition and treat it appropriately. The condition is largely not caught because healthcare providers lack the ability to diagnose the condition and treat it in the golden four-hour period after a stroke happens. An A.I. engine can quickly process the hundred or so MRI images and easily diagnose the condition, saving lives across the country.

Its clear that A.I. is a very powerful vehicle even though it is still in its nascent stage. The optimism shared by company A.I. users and governments at the meeting was very encouraging.

Over time, there is almost no endeavor that humans undertake that A.I. doesnt stand to augment, thus making humans more effective and more productive. In doing so, artificial intelligence answers the dilemma weve had for the last 20 years: a lack of productivity in the U.S. Our productivity growth has not been increasing. In fact, our productivity rate has fallen over the last 20 years and is now measured as 1 percent or less.

Productivity is the largest determinant of real wage gains. If were going to increase wealth, particularly in a sustainable and more balanced way, A.I. stands as a very important tool to dramatically increase broad productivity across almost every human endeavor.

This article is published as part of the IDG Contributor Network. Want to Join?

Peter Bendor-Samuel is founder and CEO of Everest Group, which provides advisory and research services to Global 1000 enterprises, leading service providers, and private equity firms.

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The disruption and promise of artificial intelligence - CIO