Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 2,149

Should we trust technology experts? We live in times of incredible innovations and impressive complexity. The last 30 years of technological development overturned our society, and the next 30 will likely reshape the foundations of what it means to be human. Machines have been the wealth engines of our industrial modernity, while data control and artificial intelligence will structure the power battlefields of this century.

It’s not hard then to understand why technologists, computer scientists, engineers, tech-entrepreneurs, IT experts, data analysts, etc — dress the status of champions in our age. They are shipping us into the wonders of Web 3.0, Industry 4.0, 5G communications, the blockchain transition, the G (eneticengineering). R (obotics). AI. N (anotechnologies) Revolution…and another thousand of cryptic acronyms forbidden to ordinary mortals.

We are flooded with tech-narratives. Let’s start by playing with our imaginations. What does naturally come to your mind if I say:

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As corporations struggle to fight off hackers and contain data breaches, some are looking to artificial intelligence for a solution.

They’re using machine learning to sort through millions of malware files, searching for common characteristics that will help them identify new attacks. They’re analyzing people’s voices, fingerprints and typing styles to make sure that only authorized users get into their systems. And they’re hunting for clues to figure out who launched cyberattacks—and make sure they can’t do it again.

As hackers get smarter and more determined, artificial intelligence is going to be an important part of the solution.

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This video is the second in a two-part series discussing big data. In this video, we’ll be discussing how we can utilize the vast quantities of data being generated as well as the implications of linked data on big data.

[0:33–4:43] — Starting off we’ll look at, what exactly big data is and delving deeper into the types of data, structured and unstructured and how they will be analyzed both by humans and machine learning (AI).

[4:43–11:37] — Following that we’ll discuss, how this data will be put to use and the next evolution of data, linked data, and how it will change the world and the web!

[11:37–12:37] — To conclude we’ll briefly overview the role cloud computing will play with big data!

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Computer scientists at MIT have developed a machine-learning system that can identify objects in an image based on a spoken description of the image.

Typical speech recognition systems like Google Voice and Siri rely on transcriptions of thousands of hours of speech recordings, which are then used to map speech signals to specific words.

Still in its early stages, the MIT system learns words from recorded speech clips and objects in images and then links them. Several hundred different works and objects can be recognized so far, with expectations that future versions can advance to a larger scale.

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Surging productivity and the general rise in incomes it brings would be welcome, of course, but that isn’t sufficient. The same questions being raised about the advance of robotics in the workplace apply to machine learning. While new jobs would be created, many existing jobs — from doctors and financial advisers to translators and call-center operators — are susceptible to displacement or much-reduced roles. No economic law guarantees that productivity growth benefits everyone equally. Unless we thoughtfully manage the transition, some people, even a majority, are vulnerable to being left behind even as others reap billions.

Whether it’s for the better and for the many is up to human intelligence.

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SAN FRANCISCO — Orbital Insight, a Silicon Valley geospatial analytics company, announced the purchase Sept. 18 of FeatureX, a Boston-based artificial intelligence firm specializing in computer vision for satellite imagery. The terms of the purchase were not disclosed.

It was the first acquisition made by Orbital Insight, a firm that has raised $78.7 million to date, including $50 million in a Series C funding round completed in May 2017.

FeatureX founder Gil Syswerda will join Orbital Insight as its technology research vice president, working in the company’s Boston office. FeatureX specializes in applying computer vision to satellite imagery to detect objects, enhance images and facilitate deep learning.

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Recently, there has been an explosion of interest in applying artificial intelligence (AI) to medicine. Whether explicitly or implicitly, much of this interest has centered on using AI to automate decision-making tasks that are currently done by physicians. This includes two seminal papers in the Journal of the American Medical Association demonstrating that AI-based algorithms have similar or higher accuracy than physicians: one in diagnostic assessment of metastatic breast cancer compared to pathologists and the other in detecting diabetic retinopathy compared to ophthalmologists.

While promising, these applications of AI in medicine raise a number of novel regulatory and policy issues around efficacy, safety, health workforce, and payment. They have also triggered concerns from the medical and patient communities about AI replacing doctors. And, except in narrow domains of practice, general AI systems may fall far short of the hype.

We posit that the applications of AI to “augment” physicians may be more realistic and broader reaching than those that portend to replace existing health care services. In particular, with the right support from policy makers, physicians, patients, and the technology community, we see opportunities for AI to be a solution for—rather than a contributor to—burnout among physicians and achieving the quadruple aim of improving health, enhancing the experience of care, reducing cost, and attaining joy in work for health professionals.

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