Artificial intelligence (AI) has made remarkable progress creating images that are not only breathtaking, but astonishingly diverse in style. Ten years ago, such an achievement would have been deemed unlikely by experts. Today, AI can create images using specific artistic styles, such as Van Gogh's unique approach, with an infinite range of variations. This raises an intriguing question. A very accurate, detailed, and faithful reproduction of a scene, where realism is aimed for: Ave Caesar!

Google has unveiled plans to infuse its dominant search engine with more advanced artificial intelligence technology. The move comes three months after Microsoft's Bing search engine started to tap into tech similar to that which powers the artificially intelligent chatbot ChatGPT. With our new generative AI experience in Search, you'll get even more from a single search. You'll be able to quickly make sense of information with an AI-powered snapshot, pointers to explore more and natural ways to ask. Starting at $1799, this ultra-premium device combines personal AI, #GoogleTensor G2, and @Android innovation for a #Pixel smartphone that unfolds into an incredible compact tablet.#GoogleIO

The viral success of OpenAI's ChatGPT has triggered discussions everywhere these days from the classroom to the boardroom. This amounts to a crucial moment for AI, Alice Xiang, Head of Sony Group's (SONY) AI Ethics Office and AI Lead Research Scientist, told Yahoo Finance Live (video above). "We're really seeing an inflection point with AI ethics, where it's going from being just something that companies are doing on their own… [and now] we're seeing policymakers really dive into this space." Xiang added: "This raises a lot of really interesting questions around how we ensure that we have governance processes in place to make sure AI that's built is compliant with relevant laws." As consumers encounter AI more frequently, and with excitement and fear, regulators are taking notice.

Neural Networks (NNs), or Artificial Neural Networks (ANNs), started as a big promise, and their models were quite simple compared to the models we have today: it was a simple neuron with binary outputs based on thresholds. In layman terms, it would read values as input, sum them weighted by parameters (called learning weights, where the knowledge is stored), and compared to a threshold: if it is higher, the output is one (it simulated the firing of a neuron in biology, which follows similar patterns). Except for the big hope people placed on them, they could, and still, can only separate binary boundaries: yes or no, sick or no, guilty or no. Nonetheless, do not fall prey to the common trap that simplicity as being easy: boundaries can be hard even for complex decision processes, such as release or not a patient under healthcare, or release or not a prisoner after some appeals to do so. From one side, we had some people from neuroscience seeing on the models possible explanations for their biological phenomena (i.e., in silico simulations, it was quite appealing that we could simplify the brain workings using such a simple model, based on summations). On the other hand, applied mathematical and computer scientists looking for new solutions for their complex problems out of the box (e.g., XOR problem[1], it is a problem simple for humans, but hard for machines).

A key research and development agency within the Department of Defense is accepting new contract proposals specifically focused on advancing algorithmic processing within Defense's artificial intelligence projects. The Defense Advanced Research Projects Agency is formally soliciting contracts for its new Enabling Confidence program, a subsect within its Artificial Intelligence Exploration initiative. The AIE focuses on what DARPA defines as its "third wave" of artificial intelligence research, which includes AI theory and application research that examines limitations with rule and statistical learning theories belying AI technologies. "The pace of discovery in AI science and technology is accelerating worldwide," the program announcement says. "AIE will enable DARPA to fund pioneering AI research to discover new areas where R&D programs awarded through this new approach may be able to advance the state of the art."

Imagine that data could be shared seamlessly with partners, governments, and other organisations, without breaking any data protection law, to facilitate innovation. How will it be possible to use closely guarded customer data while still maintaining the highest privacy and safety standards? Is it possible to monetise data without compromising the sensitivity of the information (or data)? The following write-up spills it all. Data is the fuel for the rapidly progressing Artificial Intelligence (AI) industry -as it is for almost all other industries. Digitisation, interconnection of the network channels, and IoT generate mountainous volumes of data at an unimagined and unprecedented scale.

Artificial Intelligence is most commonly applied in manufacturing to further develop overall equipment efficiency (OEE) and first-pass yield in production. Over time, we can utilize AI to expand uptime, work on quality and consistency, which is considered better determining. To remain cutthroat, its significant producers adjust to an additional information-driven plan of action, which is possible today with the help of AI innovations. This regularly incorporates rearrangement of staff, equipment and programming updates. Artificial Intelligence can ingest a mix of information from sensors, machines, and individuals and afterward apply it to calculations intended to upgrade tasks or accomplish lights out assembling.

The ones old enough to remember, might recall the times when image communication seemed a far distant impossible reality… a futuristic dream that would never be achieved! In the last few decades, we have seen what seems like reality surpassing imagination. Incredible technological advancements are happening at such quick pace that one is almost incapable to keep up with all the innovations. And one of the most radical inventions that is promising a tsunami in our lives is AI. The tsunami AI, has been out there actually for a long time.

On April 16, 2020, Intel and Udacity jointly announced their new Intel Edge AI for IoT Developers Nanodegree program to train the developer community in deep learning and computer vision. If you are wondering where AI is headed, now you know, it's headed to the edge. Edge computing is the concept of storing data and computing data directly at the location where it is needed. The global edge computing market is forecasted to reach 1.12 trillion dollars by 2023. Intel and Udacity aim to train 1 million developers.

Over the last decade we have seen a transformation in global pharma's commercial model as the industry rightsized itself from the blockbuster era of the '90s and early millennia. The era of competing on share of voice enabled by armies of sales representatives calling on health care professionals evolved into smarter and more sophisticated strategies for deploying sales and marketing resources--and doing more with less. Over the same period, we have witnessed a significant evolution in the rise of advanced analytics and especially the talk and the promise of Artificial Intelligence (AI) in commercial pharma, so it's time to ask the question--Can AI really help pharma proposer? McKinsey tends to think so. In a report entitled "Artificial Intelligence in Business", they concluded that AI and analytics would contribute $440 Billion in potential annual value in the pharmaceutical and medical device sector, of which the major share of over $200B resulted in value released from marketing and sales.