As per the report by International Data Corporation, almost 50 percent of the participating global organizations perceived Artificial Intelligence as a top priority. While 25 percent of these businesses had successfully implemented a company-wide AI strategy, almost 60 percent had modified their business model to accommodate AI-driven functionalities. However, one-fourth of the respondents reported that up to 50 percent of their AI projects couldn't provide the desired results. Overall, many businesses experience issues when trying to incorporate Artificial Intelligence (also known as AI) into their operations. There are various reasons for this, such as the higher costs involved, the requirement of specialist skills, and reliance on the collection of comprehensive data.

Growing number of cross-industry collaborations and partnerships and the need to control drug discovery & development costs and reduce the overall time taken in this process are the key factors driving the AI in the drug discovery market. The global AI in the drug discovery market is projected to reach USD 1,434 million by 2024 from USD 259 million in 2019, at a CAGR of 40.8% during the forecast period. Growth in this market is mainly driven by growing number of cross-industry collaborations and partnerships, the need to control drug discovery & development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications & services, and the impending patent expiry of blockbuster drugs. On the other hand, a lack of data sets in the field of drug discovery and the inadequate availability of skilled labor are some of the factors challenging the growth of the market. The immuno-oncology segment accounted for the largest share in 2019.

Massachusetts State Police (MSP) has been quietly testing ways to use the four-legged Boston Dynamics robot known as Spot, according to new documents obtained by the American Civil Liberties Union of Massachusetts. And while Spot isn't equipped with a weapon just yet, the documents provide a terrifying peek at our RoboCop future. The Spot robot, which was officially made available for lease to businesses last month, has been in use by MSP since at least April 2019 and has engaged in at least two police "incidents," though it's not clear what those incidents may have been. It's also not clear whether the robots were being operated by a human controller or how much autonomous action the robots are allowed. MSP did not respond to Gizmodo's emails on Monday morning.

Machine learning today not only recommends the things you can buy, or content you can watch, but it is doing wonders in other domains as well. This time it is on a mission to find out something untouched. There are different elements in music that trigger emotion in humans. And machine learning is trying to find out just that -- how music affects brain activity, physiological response, and human-reported behaviour. New research by scholars from the University of Southern California is trying to figure out the elements in a song that triggers different emotions in a human.

This week's episode focuses on the interface between physics and computing, with deep dives into how artificial intelligence (AI) is contributing to medical physics and how silicon could form the basis of a future quantum computer. First, we hear from Tami Freeman, Physics World's resident expert on medical physics, about a new positron emission tomography (PET) scanner that can image a patient's whole body much more quickly (or at higher resolutions) than is possible with current commercial scanners. We then stick with the medical theme to discuss three recent examples of how AI is being used in medicine: firstly to diagnose skin conditions (but, disturbingly, only if the patient's skin is white); secondly to help radiologists detect lung tumours in X-rays; and thirdly to develop better radiotherapy treatment plans. There are several ways of constructing the qubits, or quantum bits, that make up a quantum computer, and this week we hear from a trio of researchers – Fernando Gonzalez-Zalba, Alessandro Rossi and Tsung-Yeh Yang – who have been developing silicon-based qubits. Their work is part of a Europe-wide collaboration between universities, government laboratories and companies called MOS-Quito, and you can read more about it in their article for the Physics World Focus on Computing.

Agenda: 6:00 - 6:30 pm: Social Hour with Food, Drinks, Beer & Wine 6:30 - 6:35 pm: Introduction & Announcements 6:35 - 7:05 pm: Talk 1 Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry (Databricks) 7:05 - 7:35 pm: Talk 2 MLflow on and inside Azure (Microsoft) 7:35 - 8:05 pm: Talk 3 TensorFlow(X) Data Validation: Better ML through better data (Google) 8:05 - 8:30 pm: Additional Networking Talk 1 - Title: Managing the Full Deployment Lifecycle of Models with the MLflow Model Registry Presenter: Mani Parkhe, Databricks Abstract: MLflow is an open-source platform to manage the machine learning lifecycle, including experiment tracking, reproducible runs, and model packaging. In this talk, we provide an overview of the latest component of MLflow, the Model Registry, which serves as a collaborative hub where teams can share, discuss, use, inspect, and track the lineage of models. Model Registry was introduced in MLflow 1.4 and is in Private Preview on Databricks With this addition, MLflow provides end-to-end management of the deployment lifecycle of models from experimentation to online testing and production, complete with approval and governance workflows. Bio: Mani Parkhe is an ML/AI Platform Engineer at Databricks, focusing on the customer and open-source platform initiatives, which enable data discovery, training, experimentation, and deployment of ML models on the cloud. After spending 15 years building software for semiconductor chip CAD, Mani transitioned to building big data infrastructure, distributed systems and web services, and machine learning platforms.

At this week's Intel HPC Developer Conference in Denver, Colrado, USA this week, Raja Koduri, senior vice president, chief architect, and general manager of architecture, graphics and software at Intel (pictured), introduced a new category of discrete general-purpose GPUs optimised for artificial intelligence (AI) and high performance computing (HPC) convergence. The Ponte Vecchio general purpose GPU is based on the Xe architecture. "HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialised deep-learning neural network processors (NNPs), which Intel demonstrated earlier this month," said Koduri. Ponte Vecchio is architected for HPC modeling and simulation workloads and AI training. It will be manufactured on Intel's 7nm technology and will be Intel's first Xe-based GPU optimised for HPC and AI workloads.

MOTION: This House Believes AI Will Bring More Harm Than Good This debate was run in association with IBM Research. Proposition: Project Debater Project Debater is designed by IBM research. It will deliver a speech based on over 1,100 arguments collected from Union members and others over the past week. It will not be taking points of information. Sharmila Parmanand Sharmila Parmanand is a PhD Candidate in Gender Studies at the University of Cambridge and a Gates Scholar.

Could the same computer algorithms that teach autonomous cars to drive safely help identify nearby asteroids or discover life in the universe? NASA scientists are trying to figure that out by partnering with pioneers in artificial intelligence (AI)--companies such as Intel, IBM and Google--to apply advanced computer algorithms to problems in space science. Machine learning is a type of AI. It describes the most widely used algorithms and other tools that allow computers to learn from data in order to make predictions and categorize objects much faster and more accurately than a human being can. Consequently, machine learning is widely used to help technology companies recognize faces in photos or predict what movies people would enjoy.

Of all the emerging technologies that will change our daily lives, none has more transformative potential than artificial intelligence. And AI -- the use of computers to solve problems that would normally require natural, or human, intelligence -- will also have a profound effect on the global balance of economic and military power. It will change how societies are governed and people are ruled. Debates about whether China or the U.S. will dominate the 21st century are thus necessarily debates about who will lead in AI innovation, and whether democratic or authoritarian systems are better suited to that challenge. A new report from the bipartisan National Security Commission on Artificial Intelligence contains reason for cautious optimism on that latter question, even as it reminds us that an authoritarian China will be a formidable competitor indeed.