We don't see a lot of inflatable robots, which is really too bad, because they're weirdly excellent at being exactly the sort of robot that everybody seems to want: They're cheap, being made of mostly fabric and rubber, and they're very easy to fix. Relative to most other robots, they're extraordinarily lightweight, and they pack down to a small fraction of their size once deflated. Despite their squishiness, they can be strong and unexpectedly fast, since they're essentially hydraulic in nature. And perhaps most importantly, in most cases they're passively safe, since they don't have much in the way of rigid components or the inertia that comes with them. The downside of inflatable robots is that in general they're not very good at precise, repeatable control, precisely because they're so floppy.

Jobs in banking are some of the most sought after for job seekers -- but plenty of roles may not be around much longer. Despite a year of scandals that entangled many of the country's largest banks, the desire to work at these companies remains high, according to a new report by LinkedIn. Some of the more high-profile scandals include Deutsche Bank's alleged involvement in a global money-laundering scheme and accusations against Well Fargo's auto-loan and mortgage practices. Nonetheless, Bank of America, Goldman Sachs, Citigroup, Wells Fargo, and JPMorgan Chase remain five of the most popular places to work in 2019. LinkedIn attributes the popularity to banks offering increasingly tech-focused jobs that attract talented software engineers and developers out of college.

What makes artificial intelligence intelligent? Is it able to learn from errors or recognize, say, the letters of the alphabet in a set of random shapes like a human can? These are some of the questions developers of AI ask. What began as sluggish programs on hulking machines has taken the form of code that anyone in a particular field could test out and manipulate to suit their needs. Jae Ho Sohn, a radiologist at the University of California at San Francisco, is adapting and working with an AI algorithm to analyze thousands of positron emission tomography (PET) scans to search for early signs of Alzheimer's.

Many existing sports have their roots in hundreds (if not thousands) of years of human tradition. But what if you asked computers to create a sport? You now know how that can turn out. The design agency AKQA has introduced Speedgate, reportedly the first sport envisioned by an AI. The event has six-player teams competing on a field with three open-ended gates.

Across industries, the digital age has transformed the ability of companies to enhance efficiency, quality and overall operations. As technology that allows for further connectivity evolves, companies like Tetra Pak are preparing to create factories of the future. Tetra Pak, a multinational food packaging and processing company, announced at Hannover Messe 2019 that its future factory is ready to change the way food manufacturing plants operate. The company's efforts to digitalize the industry led it to partner with Microsoft, ABB, SAP and Elettric80 to create a connected packaging platform that incorporates people and artificial intelligence to transform the industry. "We are in a period where the market is changing at high speed," said Johan Nilsson, Tetra Pak vice president of Industry 4.0.

Breast cancer is the second leading cancer-related cause of death among women in the U.S. It's estimated that in 2015, 232,000 women were diagnosed with the disease and approximately 40,000 died from it. And while diagnostic exams like mammography have come into wide practice -- in 2014, over 39 million breast cancer screenings were performed in the U.S. alone -- they're not always reliable. About 10 to 15 percent of women who undergo a mammogram are asked to return following an inconclusive analysis. That's why researchers at New York University are investigating an AI-driven technique that promises much higher precision than today's tests. In a newly published paper on Arxiv.org

In the United States and Europe, the debate in the artificial intelligence community has focused on the unconscious biases of those designing the technology. Recent tests showed facial recognition systems made by companies like I.B.M. and Amazon were less accurate at identifying the features of darker-skinned people. China's efforts raise starker issues. While facial recognition technology uses aspects like skin tone and face shapes to sort images in photos or videos, it must be told by humans to categorize people based on social definitions of race or ethnicity. Chinese police, with the help of the start-ups, have done that.

AI is continuing to permeate every aspect of human society, and the field of healthcare is no different. In fact, AI is already shaping this incredibly impactful and lucrative sector in such aspects as early-intervention scans, prediction models, and in the monitoring of patients with particular diagnoses. President Trump recently signed an Executive Order prioritizing the regulation and development of AI, and with this important development firmly in mind, it is time to take a look at the future advantages and disadvantages that will likely play out as AI continues to impact upon the healthcare sector. Costs in healthcare are already spiraling out of control as costly research practices, equipment and medicines escalate the financial impact upon ordinary people. Yet the way in which costs most greatly manifest in healthcare is through human resources.

This week, the European Union published a set of ethical guidelines detailing how businesses and governments can achieve trustworthy artificial intelligence (AI)--that is, AI that is lawful, ethical, and socially and technologically robust. While these guidelines are not laws, they set out a framework for lawmakers and companies to achieve trustworthy AI. "The EU's new Ethics guidelines for trustworthy AI are a considered and constructive step toward addressing the impact of trustworthy AI on humankind, and toward laying the groundwork for necessary further discussion between key stakeholders in the private, public and governmental sectors," Juan Miguel de Joya, a consultant at the International Telecommunication Union and a member of the Association for Computing Machinery's US Technology Policy Committee, told TechRepublic. SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic) The EU's new guidelines should start conversations among businesses worldwide that may not have the resources to independently assess the impact of the technology, de Joya said. "Perhaps most fundamentally and significantly, release of the new guidelines is an opportunity for government, business, computing professionals and other stakeholders--particularly in the United States--to capture and channel the momentum of these discussions into real understanding of AI's potential and pitfalls," de Joya said. These guidelines are "a welcome, solid and significant step forward," Lorraine Kisselburgh, a visiting fellow in the Center for Education and Research in Information Assurance and Security (CERIAS) at Purdue University, and a member of the Association for Computing Machinery's US Technology Policy Committee, told TechRepublic.

Ask a layman about artificial intelligence and they might point to sci-fi villains such as HAL from 2001: A Space Odyssey or the Terminator. But the co-founders of the AI Now Institute, Meredith Whittaker and Kate Crawford, want to change the conversation. Instead of talking about far-flung super-intelligent AI, they argued on the latest episode of Recode Decode, we should be talking about the ways AI is affecting people right now, in everything from education to policing to hiring. Rather than killer robots, you should be concerned about what happens to your résumé when it hits a program like the one Amazon tried to build. "They took two years to design, essentially, an AI automatic résumé scanner," Crawford said. "And they found that it was so biased against any female applicant that if you even had the word'woman' on your résumé that it went to the bottom of the pile." That's a classic example of what Crawford calls "dirty data." Even though people think of algorithms as being ...