Hesse authorities signed a joint declaration on the establishment of an AI Quality & Testing Hub with the President of the VDE (Association of Electrical, Electronic and Information Technologies). Last week the German state and the association outlined the goal of the initiative to put AI systems to the test. Research and development, standardisation and certification are combined under one roof in the hub. In this way, the hub makes an important contribution to developing and applying AI responsibly. "AI is developing into the key technology of the 21st century, as it can offer solutions to many societal challenges. The Hessian state government wants to promote the quality of AI together with the VDE and make it verifiable. We are convinced, that the high quality of AI systems is the basis for trust and use in this technology," emphasised Hesse's digital minister Prof. Dr. Kristina Sinemus.

Founders Heu.ai 2. rpasaerialsolutions.com 3. Swiftnlift Media And Tech SwiftNLift is the Best Business Magazine across the globe for enterprises. I am really encouraged by the feedback received from the readers and the institutions which are in association with our magazine. Many thanks to my team for the work undertaken. I'm very glad to present this magazine to all the readers. The cover story has featured I Pavan Raju (Director) of Heu Technologies Private Limited. It is a platform for artificial intelligence solutions that empowers ventures to upgrade their business by solving challenging problems and enhancing them. Some print pieces have complementary components such as additional coverage, advertisements and still photography. There are regular columns by the editors with reflective articles to provide a window Artificial Intelligence and its features. Swiftnlift Magazine is not just a print or digital anymore but everything we do derives from its long-standing character, ...

It's probably no surprise that money is pouring into life sciences and healthcare startups during the biggest medical crisis in a century. CB Insights reported that global healthcare funding hit a new record $31.6 billion in this first quarter of 2021. It's also no shock that the two biggest trends – artificial intelligence and telehealth – also reaped record amounts of private cash. AI healthcare startups raised nearly $2.5 billion, while telehealth companies did even better by netting $4.2 billion in equity funding. That's the third consecutive quarter to hit record highs in both sectors dating back to Q3'20.

The Intelligence Tools & Application (ITA) group keep our Department of Defense (DoD) and Intelligence Community (IC) customers at the leading edge of technology by identifying, demonstrating, developing, and deploying emerging Machine Learning (ML) approaches to solve their complex problems. We are currently seeking an experienced Senior Machine Learning Scientist to help us develop disruptive technologies for our customers. As a Senior ML Scientist, you will apply your ML skills to wide variety of problems and datasets. Past projects include detecting disinformation campaigns from GAN-generated contents, developing algorithms to detect rare objects from overhead imagery, and inferring causal relationships between datasets. You will bring experience, creativity and entrepreneurship, and you will interact with customers, lead technical project teams, and spearhead the growth of current and new research areas.

Computer scientists have long toyed with the idea of creating computers that could write programs for other computers. Artificial intelligence is an obvious technology for the task. It has been previously used for programming on a small scale but unfortunately the results have been limited. Artificial intelligence is one of our most powerful and versatile technologies in use today. It can understand and generate speech, analyze documents, recognize images and characters, drive cars, pilot war planes, write papers, and perform thousands of other valuable operations.

In silico prediction of cardiotoxicity with high sensitivity and specificity for potential drug molecules can be of immense value. Hence, building machine learning classification models, based on some features extracted from the molecular structure of drugs, which are capable of efficiently predicting cardiotoxicity is critical. In this paper, we consider the application of various machine learning approaches, and then propose an ensemble classifier for the prediction of molecular activity on a Drug Discovery Hackathon (DDH) (1st reference) dataset. We have used only 2-D descriptors of SMILE notations for our prediction. Our ensemble classification uses 5 classifiers (2 Random Forest Classifiers, 2 Support Vector Machines and a Dense Neural Network) and uses Max-Voting technique and Weighted-Average technique for final decision. Introduction It is well known that drug discovery is complex, long drawn, and requires interdisciplinary expertise to discover new molecules. Drug safety is an important issue in the process of drug discovery. Failure in clinical trials in the 2000s was majorly due to efficacy and safety (approx 30%) (Kola, I. and Landis, J., 2004). One important aspect of drug safety is drug toxicity.

Stochastic linear contextual bandit algorithms have substantial applications in practice, such as recommender systems, online advertising, clinical trials, etc. Recent works show that optimal bandit algorithms are vulnerable to adversarial attacks and can fail completely in the presence of attacks. Existing robust bandit algorithms only work for the non-contextual setting under the attack of rewards and cannot improve the robustness in the general and popular contextual bandit environment. In addition, none of the existing methods can defend against attacked context. In this work, we provide the first robust bandit algorithm for stochastic linear contextual bandit setting under a fully adaptive and omniscient attack. Our algorithm not only works under the attack of rewards, but also under attacked context. Moreover, it does not need any information about the attack budget or the particular form of the attack. We provide theoretical guarantees for our proposed algorithm and show by extensive experiments that our proposed algorithm significantly improves the robustness against various kinds of popular attacks.

ISLAMABAD: Officials on Thurs day termed militarisation of space and artificial intelligence by India through western support an emerging threat for Pakistan's security. This was the upshot of a discussion hosted by the Islamabad Policy Institute on the occasion of the 23rd anniversary of the 1998 nuclear tests. The two panelists at the discussion titled'Pakistan's Quest for Peace and Strategic Stability in South Asia' were Adviser to Strategic Plans Division Ambassador Zamir Akram and Director General Arms Control and Disarmament at the Foreign Office Kamran Akhtar. The panelists did reiterate their concerns about the "tenuous" strategic stability of the region and India's aggressive posture, including its plans for disarming pre-emptive strikes against Pakistan's nuclear arsenal in a conflict, but they specifically emphasised the emerging challenges which, they said, could not be ignored anymore. Ambassador Akram in his presentation noted that India was working on integrating new warfare technologies -- cyberwarfare, artificial intelligence, robotics and lethal autonomous weapons -- in its arsenal with the American support.

A trademark applies to a word, design, phrase or combination thereof that identifies goods or services as coming from a certain company or source, like McDonalds' Golden Arches or Kellogg's Tony The Tiger. Registering a trademark prevents other businesses or individuals from registering the mark and secures nationwide rights for the registrant, as long as they continue using it in business. What complicates the contest between Ficker and Activision is that the party that first applies for rights over a certain mark does not necessarily grant that party the rights. Essentially, registration with the federal government is not required to gain such rights.

AI Policy Matters is a regular column in the ACM SIGAI AI Matters newsletter featuring summaries and commentary based on postings that appear twice a month in the AI Matters blog. Facial recognition (FR) issues continue to appear in the news, as well as in scholarly journal articles, while FR systems are being banned and some research is shown to be bad science. AI system researchers who try to associate facial technology output with human characteristics are sometimes referred to as machine-assisted phrenologists. Problems with FR research have been demonstrated in machine learning research such as work by Steed and Caliskan in "A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild." Meanwhile many examples of harmful products and misuses have been identified in areas such as criminality, video interviewing, and many others. Some communities have considered bans.