A new method for training robots could help streamline the ways researchers teach robots to perform simple tasks.
Category: robotics/AI – Page 919
Summary: As artificial intelligence (AI) evolves, its intersection with neuroscience stirs both anticipation and apprehension. Fears related to AI – loss of control, privacy, and human value – stem from our neural responses to unfamiliar and potentially threatening situations.
We explore how neuroscience helps us understand these fears and suggests ways to address them responsibly. This involves dispelling misconceptions about AI consciousness, establishing ethical frameworks for data privacy, and promoting AI as a collaborator rather than a competitor.
Today we’re talking to Christopher Williamson, Global Director Of Technology at TUI. We discuss Chris’s methodology of going from idea to project, the ways in which AI is being utilized to further TUI’s technology, and the ways that Joel and Chris approach technology for their kids and the next generation.
Italian fashion start-up Cap_able has launched a collection of knitted clothing that protects the wearer’s biometric data without the need to cover their face.
Named Manifesto Collection, the clothing features various patterns developed by artificial intelligence (AI) algorithms to shield the wearer’s facial identity and instead identify them as animals.
Cap_able designed the clothing with patterns – known as adversarial patches – to deceive facial recognition software in real-time.
“There were times when I felt a real emptiness,” Son told shareholders. “I had a big cry. The tears didn’t stop for days.”
Data science has been around for a long time. But the failure rates of big data projects and AI projects remain disturbingly high. And despite the hype, companies have yet to cite the contributions of data science to their bottom lines.
Why is this the case? In many companies, data scientists are not engaging in enough of softer, but more difficult, work, including gaining a deep understanding of business problems; building the trust of decision makers; explaining results in simple, powerful ways; and working patiently to address concerns among those impacted.
Managers must do four things to get more from their data science programs? First, clarify your business objectives and measure progress toward them. Second, hire data scientists best suited to the problems you face and immerse them in the day-in, day-out work of your organization. Third, demand that data scientists take end-to-end accountability for their work. Finally, insist that data scientists teach others, both inside their departments and across the company.
Cells in the human body, the building blocks of life, are arranged in a precise way. That’s necessary because pathways and spaces provide a means for cells to communicate, collaborate and function within the specific tissue or organ. Changes in cell arrangement can lead to uncontrolled cell growth, cell death and diseases, including cancer.
Scientists at the Mayo Clinic Center for Individualized Medicine and Mayo Clinic Comprehensive Cancer Center have developed an artificial intelligence method, called Spatially Informed Artificial Intelligence (SPIN-AI). This new deep-learning technique can analyze the genetic information of individual cells to reconstruct the precise layout of the cells in a tissue, without preexisting knowledge of how the cells are organized.
The new study detailing SPIN-AI is published in Biomolecules.
Who’s afraid of the big bad bots? A lot of people, it seems. The number of high-profile names that have now made public pronouncements or signed open letters warning of the catastrophic dangers of artificial intelligence is striking.
Hundreds of scientists, business leaders, and policymakers have spoken up, from deep learning pioneers Geoffrey Hinton and Yoshua Bengio to the CEOs of top AI firms, such as Sam Altman and Demis Hassabis, to the California congressman Ted Lieu and the former president of Estonia Kersti Kaljulaid.
Companies are integrating AI into their operations so quickly that job losses are likely to mount before the gains arrive. White-collar workers might be especially vulnerable in the short-term. The speed of this adoption presents an opportunity for companies to step up their pace of innovation, however — and if enough companies to go on offensive, then we won’t have to worry about AI unemployment. Adopting a bias for boldness and a startup mentality will help companies find the agility to make the most of this moment, and protect jobs as a result.
Page-utils class= article-utils—vertical hide-for-print data-js-target= page-utils data-id= tag: blogs.harvardbusiness.org, 2007/03/31:999.359084 data-title= Companies That Replace People with AI Will Get Left Behind data-url=/2023/06/companies-that-replace-people-with-ai-will-get-left-behind data-topic= AI and machine learning data-authors= Behnam Tabrizi; Babak Pahlavan data-content-type= Digital Article data-content-image=/resources/images/article_assets/2023/06/Jun23_23_1300035600-383x215.jpg data-summary=
Investing in innovation — not cutting costs — will position companies to thrive in the long run.
YouTube is currently testing a new tool that will help creators automatically dub their videos into other languages using AI, the company announced Thursday at VidCon. YouTube teamed up with AI-powered dubbing service Aloud, which is part of Google’s in-house incubator Area 120.
Earlier this year, YouTube introduced support for multi-language audio tracks, which allows creators to add dubbing to their new and existing videos, letting them reach a wider international audience. As of June 2023, creators have dubbed more than 10,000 videos in over 70 languages, the company told TechCrunch.
Previously, creators had to partner directly with third-party dubbing providers to create their audio tracks, which can be a time-consuming and expensive process. Aloud lets them dub videos at no additional cost.