Tuesday's top trending stocks come to us from every sector, from 3D manufacturing to semiconductor manufacturers to, surprisingly, a global gambling guru. Q.ai runs factor models daily to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don't have to do the digging yourself. Sign up for the free Forbes AI Investor newsletter here to join an exclusive AI investing community and get premium investing ideas before markets open. IBM IBM closed up almost 0.5% on Monday to $146.17 per share, starting off the week with nearly 7 million trades on the docket and ticking up over 16% YTD.

"Good friends, good books, and a sleepy conscience: this is the ideal life." I hope you're reading this blog in your pyjamas looking forward to a rejuvenating and healthy weekend. So, I have been working on multiple projects from creating MLE/MLOps courses to developing end-to-end ML systems at scale and I have realized that oftentimes, I am either revisiting a book that I've read or I'm referring to a book that I just skimmed through but never got the chance to really read it. This week, I want to share with you the books that I personally feel every ML Enthusiast and Practitioner should read to get a sense of the breadth(ideas) and depth(grok) of this field respectively. It is a short and crisp list covering a majority of ML topics.

Ever wondered what happens to a selfie you upload on a social media site? Activists and researchers have long warned about data privacy and said that photographs uploaded on the Internet may be used to train artificial intelligence (AI) powered facial recognition tools. These AI-enabled tools (such as Clearview, AWS Rekognition, Microsoft Azure, and Face) could in turn be used by governments or other institutions to track people and even draw conclusions such as the subject's religious or political preferences. Researchers have come up with ways to dupe or spoof these AI tools from being able to recognise or even detect a selfie, using adversarial attacks – or a way to alter input data that causes a deep-learning model to make mistakes. Two of these methods were presented last week at the International Conference of Learning Representations (ICLR), a leading AI conference that was held virtually.

In March 1950, an RAF wing commander and trained accountant named Charles Reep turned his eye for numbers to soccer. Reep, who had become interested in the sport in the 1930s and was fascinated by Herbert Chapman's pioneering Arsenal team, had returned from the Second World War to find that the tactical revolution he'd witnessed before had stalled. This story originally appeared on WIRED UK. Finally, at half-time during a drab Division Three game between Swindon Town and Bristol City, during which he watched countless attacks amount to nothing, Reep's patience ran out. He grabbed a notebook and a pencil and began furiously jotting down what happened on the pitch: He started counting the number of passes and shots in one of the first systematic attempts to use data to analyze soccer.

Fractal's Computer Vision solution for Underwriting & Claims selected as one of the top solutions to be presented at the event to be attended by more than 400 P&C Execs Fractal's Chief Practice Officer for Insurance and Technology, Sankar Narayanan, will be speaking at "Tech Showcase: Computer Vision for Underwriting & Claims". The event will bring together over 400 top executives and insiders for discussions and presentations on some of the most pressing needs and innovative solutions. The talk will feature Fractal's Image and Video Analytics (IVA) solution and demonstrate how it enables #betterdecisions for P&C Insurers. IVA is Fractal's deep-learning based solution that is used by insurers to analyze images/videos/complex documents at scale, during underwriting and claims stages. During underwriting, it is used to assess value and estimate coverage with precision and during claims it is used to assess severity and estimate settlement amount, at speed.

Despite years of hype, virtual reality headsets have yet to topple TV or computer screens as the go-to devices for video viewing. One reason: VR can make users feel sick. Nausea and eye strain can result because VR creates an illusion of 3D viewing although the user is in fact staring at a fixed-distance 2D display. The solution for better 3D visualization could lie in a 60-year-old technology remade for the digital world: holograms. Holograms deliver an exceptional representation of 3D world around us.

That element of randomness is partially why GPT-3--or its less powerful predecessor, GPT-2--is taking time to gain widespread commercial traction as a tool to power chatbots or auto-generate ads. After nearly a year of experimentation, however, how such a technology might be tamed for marketing purposes is beginning to take shape. Working through OpenAI's closely guarded API program, startups and agency technologists have reined in GPT-3's more eccentric tendencies, which can range from nonsensical prose to inappropriate or explicit content, in order to put it to use for rote performance marketing tasks like A/B testing endless variations of a digital ad, generating product descriptions or assigning email subject lines. Meanwhile, other companies are capitalizing on GPT-3's stranger side for creative tools. While still nascent, projects like these offer a glimpse into a future where humans might work hand in hand with generative AI on creative copywriting and the give-and-take forces that might define such a relationship.

Despite years of hype, virtual reality headsets have yet to topple TV or computer screens as the go-to devices for video viewing. One reason: VR can make users feel sick. Nausea and eye strain can result because VR creates an illusion of 3D viewing although the user is in fact staring at a fixed-distance 2D display. The solution for better 3D visualization could lie in a 60-year-old technology remade for the digital world: holograms. Holograms deliver an exceptional representation of 3D world around us.

Smart Cities and Communities (SCC) constitute a new paradigm in urban development. SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control. Perception, Smart Traffic Control and Driver Modelling are described along with open research challenges and standardization to help introduce advanced driver assistance systems and automated vehicle functionality in traffic. To fully realize the potential of SCC, to create a holistic view on a city level, the availability of data from different stakeholders is need. Further, though AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to match function requirements with the level of detail in data annotation in order to train an accurate model. Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusion it is difficult for humans to trust it.

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementation of the original environment. Besides, we provide users with new APIs to access a joint control mode, image observations and goals with customisable camera and a built-in on-hand camera. We further design a set of multi-step, multi-goal, long-horizon and sparse reward robotic manipulation tasks, aiming to inspire new goal-conditioned reinforcement learning algorithms for such challenges. We use a simple, human-prior-based curriculum learning method to benchmark the multi-step manipulation tasks. Discussions about future research opportunities regarding this kind of tasks are also provided.