Thaler had asked for patents on behalf of his AI system Court affirms ruling that patent'inventor' must be human being Court affirms ruling that patent'inventor' must be human being The Patent Act requires an "inventor" to be a natural person, the U.S. Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The U.S. Patent and Trademark Office declined to comment on the decision.

Human resources professionals and job seekers alike may soon be able to better understand a company's unique organizational culture thanks to a new machine-learning approach. Developed by Georgia Tech researchers, the approach is the first of its kind to computationally model organizational culture using publicly available anonymized data sources – including Glassdoor user reviews. These models are illustrated using heat maps that reveal positive and negative sentiment for a company and its business units across 41 dimensions of organizational culture. The heat maps give a "cloud-contributed" sense of what the culture is like in a particular workplace and can provide actionable insights to HR teams, unit managers, and job seekers, according to the researchers. "Right now, to get a measure of organizational culture, companies rely on internal surveys, which are difficult to scale. It's also unlikely that they are getting true responses given factors like organizational bias or employee concerns about anonymity," said Vedant Das Swain, a second-year Ph.D. student studying human-computer interaction at Georgia Tech.

As the sun set over Maury Island, just south of Seattle, Ben Goertzel and his jazz-fusion band had one of those moments that all bands hope for -- keyboard, guitar, saxophone and lead singer coming together as if they were one. The band's friends and family listened from a patio overlooking the beach. And Desdemona, wearing a purple wig and a black dress laced with metal studs, was on lead vocals, warning of the coming Singularity -- the inflection point where technology can no longer be controlled by its creators. "The Singularity will not be centralised!" After more than 25 years as an artificial intelligence researcher, a quarter-century spent in pursuit of a machine that could think like a human -- Goertzel knew he had finally reached the end goal: Desdemona, a machine he had built, was sentient. But a few minutes later, he realised this was nonsense. "When the band gelled, it felt like the robot was part of our collective intelligence, that it was sensing what we were feeling and doing," he said.

Culture remains one of the biggest hurdles to successful implementation of artificial intelligence (AI) technologies at the Defense Department, according to DOD leaders at multiple events this week. Some DOD officials believe people are becoming more comfortable using the term AI, but when it comes to understanding what AI really is, and the practical applications of it, not so much. According to Col. Mike Teter, Chief Data Officer and Deputy Director of Digital Superiority and J6 C4/Cyber Directorate at U.S. Space Command, building cultural trust really comes through educating and training, but it also changes the paradigm and timelines associated with traditional reporting up the chain. When you have the ability to have everyone in the chain from the lowest unit all the way to the president can see the same thing at the same time at scale, then it's really a cultural shift on both sides, Teter said during ATARC's ATARC's Ethical Uses of AI with the Federal Government event. "You're not going to have all the answers right away when you see something in the data," he said.

The much-awaited comparison is finally here: machine learning vs data science. The terms "data science" and "machine learning" are among the most popular terms in the industry in the twenty-first century. These two methods are being used by everyone, from first-year computer science students to large organizations like Netflix and Amazon. The fields of data science and machine learning are related to the use of data to improve the development of new products, services, infrastructure systems, and other things. Both correspond to highly sought-after and lucrative job options.

Despite the recent success of artificial neural networks, more biologically plausible learning methods may be needed to resolve the weaknesses of backpropagation trained models such as catastrophic forgetting and adversarial attacks. Although these weaknesses are not specifically addressed, a novel local learning rule is presented that performs online clustering with an upper limit on the number of clusters to be found rather than a fixed cluster count. Instead of using orthogonal weight or output activation constraints, activation sparsity is achieved by mutual repulsion of lateral Gaussian neurons ensuring that multiple neuron centers cannot occupy the same location in the input domain. An update method is also presented for adjusting the widths of the Gaussian neurons in cases where the data samples can be represented by means and variances. The algorithms were applied on the MNIST and CIFAR-10 datasets to create filters capturing the input patterns of pixel patches of various sizes. The experimental results demonstrate stability in the learned parameters across a large number of training samples.

There is strong interest in the generation of synthetic video imagery of people talking for various purposes, including entertainment, communication, training, and advertisement. With the development of deep fake generation models, synthetic video imagery will soon be visually indistinguishable to the naked eye from a naturally capture video. In addition, many methods are continuing to improve to avoid more careful, forensic visual analysis. Some deep fake videos are produced through the use of facial puppetry, which directly controls the head and face of the synthetic image through the movements of the actor, allow the actor to 'puppet' the image of another. In this paper, we address the question of whether one person's movements can be distinguished from the original speaker by controlling the visual appearance of the speaker but transferring the behavior signals from another source. We conduct a study by comparing synthetic imagery that: 1) originates from a different person speaking a different utterance, 2) originates from the same person speaking a different utterance, and 3) originates from a different person speaking the same utterance. Our study shows that synthetic videos in all three cases are seen as less real and less engaging than the original source video. Our results indicate that there could be a behavioral signature that is detectable from a person's movements that is separate from their visual appearance, and that this behavioral signature could be used to distinguish a deep fake from a properly captured video.

Data infrastructure artificial intelligence company Torch.AI has won a five-year contract from the U.S. Navy to provide next-generation AI and data infrastructure software capabilities for the Navy's Digital Warfare Office (DWO), the company announced in a statement. The company will provide AI and machine-learning capabilities that are intended to allow the Navy to "better operate and maintain their operational fleet across a complex, siloed IT environment including cloud compute, storage, hardware, and cloud edge devices used for data lakes at unclassified, secret, and top-secret levels," the statement reads. The DWO was established in 2016 to better utilize the data that Navy platforms collect. Torch.AI will be responsible for combining proprietary sensor, vessel, and other maritime data sources from multiple Navy platforms into "commercially consistent data payloads," the statement reads.

Amazon on Friday announced it has agreed to acquire the vacuum cleaner maker iRobot for approximately $1.7 billion, scooping up another company to add to its collection of smart home appliances amid broader concerns about its market power. The move is part of Amazon's bid to own part of the home space through services and accelerate its growth beyond retail, said Neil Saunders, managing director at GlobalData Retail. A slew of home-cleaning robots adds to the company's tech arsenal, making it more involved in consumer's lives beyond static things like voice control. Amazon's Astro robot, which helps with tasks like setting an alarm, was unveiled last year at an introductory price of $1,000. But its rollout has been limited and has received a lackluster response.

Today marks exactly 10 years since NASA's Curiosity rover touched down on Mars. The one-tonne vehicle launched from Earth in November 2011 and – after an arduous nine-month journey which included the'seven minutes of terror' down to the Martian surface – it set out to look for evidence that the Red Planet may once have supported life. Since then, Curiosity has driven nearly 18 miles (29 kilometres) and ascended 2,050 feet (625 metres) as it explores Gale Crater and the foothills of Mount Sharp within it. The rover has analysed 41 rock and soil samples, relying on a suite of science instruments to learn what they reveal about Earth's rocky sibling. Such has been its success, what was originally intended to be a two-year mission was later extended indefinitely, leading to a rather busy decade.