The new bill, called the AI Liability Directive, will add teeth to the EU's AI Act, which is set to become EU law around the same time. The AI Act would require extra checks for "high risk" uses of AI that have the most potential to harm people, including systems for policing, recruitment, or health care. The new liability bill would give people and companies the right to sue for damages after being harmed by an AI system. The goal is to hold developers, producers, and users of the technologies accountable, and require them to explain how their AI systems were built and trained. Tech companies that fail to follow the rules risk EU-wide class actions.

After months of tweets and teases, Elon Musk Tesla's new humanoid Optimus robot was slammed by AI and robotics experts during its unveiling on Friday. Despite the high-octane lights and music that surrounded it, the company's human-shaped robot did not deliver bells and whistles at its AI Day presentation in Palo Alto, California. 'None of this is cutting edge,' tweeted robotics expert Cynthia Yeung. 'Hire some PhDs and go to some robotics conferences @Tesla.' The robot was meant to be the star for the tech giant's conference, as Musk claimed it would'be a fundamental transformation for civilization as we know it.'

Artificial intelligence is getting better and better at generating an image in response to a handful of words, with publicly available AI image generators such as DALL-E 2 and Stable Diffusion. Now, Meta researchers are taking AI a step further: they're using it to concoct videos from a text prompt. Meta CEO Mark Zuckerberg posted on Facebook on Thursday about the research, called Make-A-Video, with a 20-second clip that compiled several text prompts that Meta researchers used and the resulting (very short) videos. The prompts include "A teddy bear painting a self portrait," "A spaceship landing on Mars," "A baby sloth with a knitted hat trying to figure out a laptop," and "A robot surfing a wave in the ocean." The videos for each prompt are just a few seconds long, and they generally show what the prompt suggests (with the exception of the baby sloth, which doesn't look much like the actual creature), in a fairly low-resolution and somewhat jerky style.

AI has the potential to deliver enormous business value for organizations, and its adoption has been sped up by the data-related challenges of the pandemic. Forrester estimates that almost 100% of organizations will be using AI by 2025, and the artificial intelligence software market will reach $37 billion by the same year. But there is growing concern around AI bias -- situations where AI makes decisions that are systematically unfair to particular groups of people. Researchers have found that AI bias has the potential to cause real harm. I recently had the chance to speak with Ted Kwartler, VP of Trusted AI at DataRobot, to get his thoughts on how AI bias occurs and what companies can do to make sure their models are fair.

How healthcare is evolving with the help of AI/ML? Data analysis and improve diagnosis: AI-enabled technology can evaluate data at a faster rate than that of any human, which include clinical tests, medical history, and genetic analysis that can aid healthcare professionals in determining a patient's condition. Administrative and routine tasks: Routine tasks such as record keeping, data management, and scan analysis, can be automated with AI. Without much time being spent on manual processes, healthcare professionals can allocate more resources to patient care. Drug Development: Developing a new drug takes a lengthy time.

This discussion paper demonstrates how longitudinal sentiment analyses can depict intertemporal dynamics on social media platforms, what challenges are inherent and how further research could benefit from a longitudinal perspective. Furthermore and since tools for sentiment analyses shall simplify and accelerate the analytical process regarding qualitative data at acceptable inter-rater reliability, their applicability in the context of radicalization research will be examined regarding the Tweets collected on January 6th 2021, the day of the storming of the U.S. Capitol in Washington. Therefore, a total of 49,350 Tweets will be analyzed evenly distributed within three different sequences: before, during and after the U.S. Capitol in Washington was stormed. These sequences highlight the intertemporal dynamics within comments on social media platforms as well as the possible benefits of a longitudinal perspective when using conditional means and conditional variances. Limitations regarding the identification of supporters of such events and associated hate speech as well as common application errors will be demonstrated as well. As a result, only under certain conditions a longitudinal sentiment analysis can increase the accuracy of evidence based predictions in the context of radicalization research.

In this study, we propose parameter-varying neural ordinary differential equations (NODEs) where the evolution of model parameters is represented by partition-of-unity networks (POUNets), a mixture of experts architecture. The proposed variant of NODEs, synthesized with POUNets, learn a meshfree partition of space and represent the evolution of ODE parameters using sets of polynomials associated to each partition. We demonstrate the effectiveness of the proposed method for three important tasks: data-driven dynamics modeling of (1) hybrid systems, (2) switching linear dynamical systems, and (3) latent dynamics for dynamical systems with varying external forcing.

Tensions inside the Israeli occupied West Bank are continuing to escalate, in a year of violence not witnessed inside the territory since 2007. Following a lethal Israeli invasion of Jenin refugee camp, in the north of the West Bank, resulting in 4 Palestinians being killed and 44 injured, Israel's military seems to have gotten the green-light for even greater horrors. The chief of staff for the Israeli army, Aviv Kohavi, was reported to have permitted the usage of attack drones to launch airstrike assassinations inside the territory, something that had previously only taken place in the Gaza Strip. Even more shocking, in the past weeks, Palestinians have noted another appalling development; Israel is deploying AI-powered guns at checkpoints and on military vehicles. The most prominently reported instance of an AI-rifle system being deployed is the system at an Israeli checkpoint in the city of Al-Khalil (Hebron).

According to the World Cancer Day the key cancer facts are that 10 million people die from cancer every year, at least one third of common cancers are preventable, cancer is the second-leading cause of death worldwide, 70% of cancer deaths occur in low-to-middle income countries, millions of lives could be saved each year by implementing strategies for prevention, early detection and treatment, and the total annual economic cost of cancer is estimated at $ 1.16 trillion. For all the above reasons, AI and ML techniques are breaking into cancer research and oncology, where the potential applications are vast, and include early detection and diagnosis of cancer, subtype classification of cancer, optimisation of cancer treatment and identification of new therapeutic targets. In particular, AI is predicted to change cancer health care by advancing clinical research and drug development. And besides cutting costs, improving trial quality and reducing trial times by almost half, AI is predicted to find novel cancer biomarkers and gene signatures, recruit eligible clinical trial patients in minutes and read volumes of text in seconds. Moreover, breakthrough discoveries involving new diagnostic tools for cancer have seen AI as a major player. The phrase "prevention is better than cure" is often attributed to the Dutch philosopher Desiderius Erasmus in around 1500, but prevention is now synonym to "it's cheaper too", since preventing future illnesses and complications is vital to the future sustainability of health systems and households as well.

Whether pursuing academia or industry, this degree uniquely positions students for the future of research and high demand careers with a mastery of integrating engineering domain knowledge into AI solutions. Our master's programs are self-supported or have an outside funding source (such as the student's employer) to pay for tuition and living expenses. To see annual tuition rates, visit Carnegie Mellon's The HUB website Note that immigration regulations do not allow Carnegie Mellon University to issue visa documents for part-time master's programs.