What’s the biggest barrier to successful artificial intelligence (A.I.) and machine-learning projects?
Earlier this year, Arvind Krishna, IBM’s senior vice president of cloud and cognitive software, suggested that such initiatives tend to fail once companies realize the expense and labor involved in collecting and structuring data for analysis. “And so you run out of patience along the way, because you spend your first year just collecting and cleansing the data,” he told the audience at The Wall Street Journal’s Future of Everything Festival, according to the newspaper.
“And you say: ‘Hey, wait a moment, where’s the A.I.? I’m not getting the benefit.’ And you kind of bail on it,” he reportedly added. Read More
Daily Archives: October 24, 2019
How to Turn a Quantum Computer Into the Ultimate Randomness Generator
Say the words “quantum supremacy” at a gathering of computer scientists, and eyes will likely roll. The phrase refers to the idea that quantum computers will soon cross a threshold where they’ll perform with relative ease tasks that are extremely hard for classical computers. Until recently, these tasks were thought to have little real-world use, hence the eye rolls.
But now that Google’s quantum processor is rumored to be close to reaching this goal, imminent quantum supremacy may turn out to have an important application after all: generating pure randomness. Read More
Why artificial intelligence doesn't really exist yet
The processes underlying artificial intelligence today are in fact quite dumb. Researchers from Bochum are attempting to make them smarter.
Radical change, revolution, megatrend, maybe even a risk: artificial intelligence has penetrated all industrial segments and keeps the media busy. Researchers at the RUB Institute for Neural Computation have been studying it for 25 years. Their guiding principle is: in order for machines to be truly intelligent, new approaches must first render machine learning more efficient and flexible.
“There are two types of machine learning that are successful today: deep neural networks, also known as Deep Learning, as well as reinforcement learning,” explains Professor Laurenz Wiskott, Chair for Theory of Neuronal Systems. Read More
First-ever humanoid robot powered by cloud artificial intelligence
Who needs to use that delicate tiny sewing staple, when there’s now a robot that can thread a needle for you? CloudMinds XR-1, 5G Humanoid Robots with vision-controlled grasping tech and intricate manual tasks, interacted with guests at the Sprint exhibit at the Mobile World Congress 2019 Los Angeles, (MWC19) in Los Angeles.
The XR-1 robot is powered by cloud artificial intelligence (AI)–one of the first of its kind–Sprint True Mobile 5G, and proprietary vision-controlled grasping tech, which means it not only can thread a needle, but can serve drinks and can be programmed to do other tasks, including manufacturing. Read More
Reframing Superintelligence — Comprehensive AI Services as General Intelligence
Studies of superintelligent-level systems have typically posited AI func-tionality that plays the role of a mind in a rational utility-directed agent,and hence employ an abstraction initially developed as an idealized model of human decision makers. Today, developments in AI technology highlight intelligent systems that are quite unlike minds, and provide a basis for a different approach to understanding them: Today, we can consider how AI systems are produced (through the work of research and development), what they do (broadly, provide services by performing tasks), and what they will enable (including incremental yet potentially thorough automation of human tasks).
Because tasks subject to automation include the tasks that comprise AI research and development, current trends in the field promise accelerating AI-enabled advances in AI technology itself, potentially leading to asymptotically recursive improvement of AI technologies in distributed systems, a prospect that contrasts sharply with the vision of self-improvement internal to opaque, unitary agents. Read More
Trust, control and personalization through human-centric AI
Our virtual lives lie in the hands of algorithms that govern what we see and don’t see, how we perceive the world and which life choices we make. Artificial intelligence decides which movies are of interest to you, how your social media feeds should look like, and which advertisements have the highest likelihood of convincing you. These algorithms are either controlled by corporations or by governments, each of which tend to have goals that differ from the individual’s objectives.
In this article, we dive into the world of human-centric AI, leading to a new era where the individual not only controls the data, but also steers the algorithms to ensure fairness, privacy and trust. Breaking free from filter bubbles and detrimental echo chambers that skew the individual’s worldview allows the user to truly benefit from today’s AI revolution.
While the devil is in the implementation and many open questions still remain, the main purpose of this think piece is to spark a discussion and lay out a vision of how AI can be employed in a human-centric way. Read More
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