2 items tagged "future"

  • Exploring the risks of artificial intelligence

    shutterstock 117756049“Science has not yet mastered prophecy. We predict too much for the next year and yet far too little for the next ten.”

    These words, articulated by Neil Armstrong at a speech to a joint session of Congress in 1969, fit squarely into most every decade since the turn of the century, and it seems to safe to posit that the rate of change in technology has accelerated to an exponential degree in the last two decades, especially in the areas of artificial intelligence and machine learning.

    Artificial intelligence is making an extreme entrance into almost every facet of society in predicted and unforeseen ways, causing both excitement and trepidation. This reaction alone is predictable, but can we really predict the associated risks involved?

    It seems we’re all trying to get a grip on potential reality, but information overload (yet another side affect that we’re struggling to deal with in our digital world) can ironically make constructing an informed opinion more challenging than ever. In the search for some semblance of truth, it can help to turn to those in the trenches.

    In my continued interview with over 30 artificial intelligence researchers, I asked what they considered to be the most likely risk of artificial intelligence in the next 20 years.

    Some results from the survey, shown in the graphic below, included 33 responses from different AI/cognitive science researchers. (For the complete collection of interviews, and more information on all of our 40+ respondents, visit the original interactive infographic here on TechEmergence).

    Two “greatest” risks bubbled to the top of the response pool (and the majority are not in the autonomous robots’ camp, though a few do fall into this one). According to this particular set of minds, the most pressing short- and long-term risks is the financial and economic harm that may be wrought, as well as mismanagement of AI by human beings.

    Dr. Joscha Bach of the MIT Media Lab and Harvard Program for Evolutionary Dynamics summed up the larger picture this way:

    “The risks brought about by near-term AI may turn out to be the same risks that are already inherent in our society. Automation through AI will increase productivity, but won’t improve our living conditions if we don’t move away from a labor/wage based economy. It may also speed up pollution and resource exhaustion, if we don’t manage to install meaningful regulations. Even in the long run, making AI safe for humanity may turn out to be the same as making our society safe for humanity.”

    Essentially, the introduction of AI may act as a catalyst that exposes and speeds up the imperfections already present in our society. Without a conscious and collaborative plan to move forward, we expose society to a range of risks, from bigger gaps in wealth distribution to negative environmental effects.

    Leaps in AI are already being made in the area of workplace automation and machine learning capabilities are quickly extending to our energy and other enterprise applications, including mobile and automotive. The next industrial revolution may be the last one that humans usher in by their own direct doing, with AI as a future collaborator and – dare we say – a potential leader.

    Some researchers believe it’s a matter of when and not if. In Dr. Nils Nilsson’s words, a professor emeritus at Stanford University, “Machines will be singing the song, ‘Anything you can do, I can do better; I can do anything better than you’.”

    In respect to the drastic changes that lie ahead for the employment market due to increasingly autonomous systems, Dr. Helgi Helgason says, “it’s more of a certainty than a risk and we should already be factoring this into education policies.”

    Talks at the World Economic Forum Annual Meeting in Switzerland this past January, where the topic of the economic disruption brought about by AI was clearly a main course, indicate that global leaders are starting to plan how to integrate these technologies and adapt our world economies accordingly – but this is a tall order with many cooks in the kitchen.

    Another commonly expressed risk over the next two decades is the general mismanagement of AI. It’s no secret that those in the business of AI have concerns, as evidenced by the $1 billion investment made by some of Silicon Valley’s top tech gurus to support OpenAI, a non-profit research group with a focus on exploring the positive human impact of AI technologies.

    “It’s hard to fathom how much human-level AI could benefit society, and it’s equally hard to imagine how much it could damage society if built or used incorrectly,” is the parallel message posted on OpenAI’s launch page from December 2015. How we approach the development and management of AI has far-reaching consequences, and shapes future society’s moral and ethical paradigm.

    Philippe Pasquier, an associate professor at Simon Fraser University, said “As we deploy more and give more responsibilities to artificial agents, risks of malfunction that have negative consequences are increasing,” though he likewise states that he does not believe AI poses a high risk to society on its own.

    With great responsibility comes great power, and how we monitor this power is of major concern.

    Dr. Pei Wang of Temple University sees major risk in “neglecting the limitations and restrictions of hot techniques like deep learning and reinforcement learning. It can happen in many domains.” Dr. Peter Voss, founder of SmartAction, expressed similar sentiments, stating that he most fears “ignorant humans subverting the power and intelligence of AI.”

    Thinking about the risks associated with emerging AI technology is hard work, engineering potential solutions and safeguards is harder work, and collaborating globally on implementation and monitoring of initiatives is the hardest work of all. But considering all that’s at stake, I would place all my bets on the table and argue that the effort is worth the risk many times over.

    Source: Tech Crunch

  • Where Artificial Intelligence Is Now and What’s Just Around the Corner

    artificial-intelligence-predictions-2-234x156Unexpected convergent consequences...this is what happens when eight different exponential technologies all explode onto the scene at once.

    This post (the second of seven) is a look at artificial intelligence. Future posts will look at other tech areas.

    An expert might be reasonably good at predicting the growth of a single exponential technology (e.g., the Internet of Things), but try to predict the future when A.I., robotics, VR, synthetic biology and computation are all doubling, morphing and recombining. You have a very exciting (read: unpredictable) future. ​ This year at my Abundance 360 Summit I decided to explore this concept in sessions I called "Convergence Catalyzers."

    For each technology, I brought in an industry expert to identify their Top 5 Recent Breakthroughs (2012-2015) and their Top 5 Anticipated Breakthroughs (2016-2018). Then, we explored the patterns that emerged.

    Artificial Intelligence — Context

    At A360 this year, my expert on AI was Stephen Gold, the CMO and VP of Business Development and Partner Programs at IBM Watson. Here's some context before we dive in.

    Artificial intelligence is the ability of a computer to understand what you're asking and then infer the best possible answer from all the available evidence.

    You may think of AI as Siri or Google Now on your iPhone, Jarvis from Iron Man or IBM's Watson.

    Progress of late is furious — an AI R&D arms race is underway among the world's top technology giants.

    Soon AI will become the most important human collaboration tool ever created, amplifying our abilities and providing a simple user interface to all exponential technologies. Ultimately, it's helping us speed toward a world of abundance.

    The implications of true AI are staggering, and I asked Stephen to share his top five breakthroughs from recent years to illustrate some of them.

    Recent Top 5 Breakthroughs in AI: 2011 - 2015

    "It's amazing," said Gold. "For 50 years, we've ideated about this idea of artificial intelligence. But it's only been in the last few years that we've seen a fundamental transformation in this technology."

    Here are the breakthroughs Stephen identified in artificial intelligence research from 2011-2015:

    1. IBM Watson wins Jeopardy demo's integration of natural language processing, machine learning (ML), and big data.

    In 2011, IBM's AI system, dubbed "Watson," won a game of Jeopardy against the top two all-time champions.

    This was a historic moment, the "Kitty Hawk moment" for artificial intelligence.

    "It was really the first substantial, commercial demonstration of the power of this technology," explained Gold. "We wanted to prove a point that you could bring together some very unique technologies: natural language technologies, artificial intelligence, the context, the machine learning and deep learning, analytics and data and do something purposeful that ideally could be commercialized."

    2. Siri/Google Now redefine human-data interaction.

    In the past few years, systems like Siri and Google Now opened our minds to the idea that we don't have to be tethered to a laptop to have seamless interaction with information.

    In this model, AIs will move from speech recognition to natural language interaction, to natural language generation, and eventually to an ability to write as well as receive information.

    3. Deep learning demonstrates how machines learn on their own, advance and adapt.

    "Machine learning is about man assisting computers. Deep learning is about systems beginning to progress and learn on their own," says Gold. "Historically, systems have always been trained. They've been programmed. And, over time, the programming languages changed. We certainly moved beyond FORTRAN and BASIC, but we've always been limited to this idea of conventional rules and logic and structured data."

    As we move into the area of AI and cognitive computing, we're exploring the ability of computers to do more unaided/unassisted learning.

    4. Image recognition and interpretation now rivals what humans can do — allowing for imagine interpretation and anomaly detection.

    Image recognition has exploded over the last few years. Facebook and Google Photos, for example, each have tens of billions of images on their platform. With this dataset, they (and many others) are developing technologies that go beyond facial recognition providing algorithms that can tell you what is in the image: a boat, plane, car, cat, dog, and so on.

    The crazy part is that the algorithms are better than humans at recognizing images. The implications are enormous. "Imagine," says Gold, "an AI able to examine an X-ray or CAT scan or MRI to report what looks abnormal."

    5. AI Apps proliferate: universities scramble to adopt AI curriculum

    As AI begins to impact every industry and every profession, there is a response where schools and universities are ramping up their AI and machine learning curriculum. IBM, for example, is working with over 150 partners to present both business and technology-oriented students with cognitive computing curricula.

    So what's in store for the near future?

    Anticipated Top AI Breakthroughs: 2016 – 2018

    Here are Gold's predictions for the most exciting, disruptive developments coming in AI in the next three years. As entrepreneurs and investors, these are the areas you should be focusing on, as the business opportunities are tremendous.

    1. Next-gen A.I. systems will beat the Turing Test

    Alan Turing created the Turing Test over half a century ago as a way to determine a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

    Loosely, if an artificial system passed the Turing Test, it could be considered "AI."

    Gold believes, "that for all practical purposes, these systems will pass the Turing Test" in the next three-year period.

    Perhaps more importantly, if it does, this event will accelerate the conversation about the proper use of these technologies and their applications.

    2. All five human senses (yes, including taste, smell and touch) will become part of the normal computing experience.

    AIs will begin to sense and use all five senses. "The sense of touch, smell, and hearing will become prominent in the use of AI," explained Gold. "It will begin to process all that additional incremental information."

    When applied to our computing experience, we will engage in a much more intuitive and natural ecosystem that appeals to all of our senses.

    3. Solving big problems: detect and deter terrorism, manage global climate change.

    AI will help solve some of society's most daunting challenges.

    Gold continues, "We've discussed AI's impact on healthcare. We're already seeing this technology being deployed in governments to assist in the understanding and preemptive discovery of terrorist activity."

    We'll see revolutions in how we manage climate change, redesign and democratize education, make scientific discoveries, leverage energy resources, and develop solutions to difficult problems.

    4. Leverage ALL health data (genomic, phenotypic, social) to redefine the practice of medicine.

    "I think AI's effect on healthcare will be far more pervasive and far quicker than anyone anticipates," says Gold. "Even today, AI/machine learning is being used in oncology to identify optimal treatment patterns."

    But it goes far beyond this. AI is being used to match clinical trials with patients, drive robotic surgeons, read radiological findings and analyze genomic sequences.

    5. AI will be woven into the very fabric of our lives — physically and virtually.

    Ultimately, during the AI revolution taking place in the next three years, AIs will be integrated into everything around us, combining sensors and networks and making all systems "smart."

    AIs will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalized and easy to use. We'll be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

    Source: SingularityHub

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