A Dragons' Den-style event this week, where tech companies will have 20 minutes to pitch ideas for increasing automation in the British justice system, is one of numerous examples of how the cash-strapped Labour government hopes artificial intelligence and data science can save money and improve public services. Amid warnings from critics that Downing Street has been "drinking the Kool-Aid" on AI, the Department of Health and Social Care this week announced an AI early warning system to detect dangerous maternity services after a series of scandals, and Wes Streeting, the health secretary, said he wants one in eight operations to be conducted by a robot within a decade. AI is being used to prioritise actions on the 25,000 pieces of correspondence the Department for Work and Pensions receives each day and to detect potential fraud and error in benefit claims. Ministers even have access to an AI tool that is supposed to provide a "vibe check" on parliamentary opinion to help them weigh the political risks of policy proposals. Again and again, ministers are turning to technology to tackle acute crises that in the past might have been dealt with by employing more staff or investing more money.

The tech industry today is abuzz with the potential of AI in healthcare – this discussion is drawing in the interest of very diverse stakeholders, from healthcare providers to drug developers to health insurers and to general public at large. This is not merely hype – the kind of substantial investments pouring in are a proof. According to Accenture, the AI healthcare market is projected to reach $6.6 billion by 2021, expected to result in about $150 billion cost savings annually. Accenture also broke down potential annual benefits for 2026 within the healthcare industry where robot-assisted surgery could easily cut costs by $40 billion while virtual nursing assistants, dosage error reduction, clinical trials and automated image diagnosis could save $20 billion, $16 billion, $13 billion and $3 billion respectively. Gurpreet Singh, a U.S. health services leader at PWC, sees three main areas where major chunks of investment will be heavily focused; digitization, engagement and diagnostics.

More fast food chains than ever are testing robots and AI to cut costs. Advanced technology can be used to decrease the number of workers needed for food preparation and service. Robots are being used to take orders, prepare food, and even deliver it to customers. More fast food chains than ever are testing robots and AI to cut costs. Advanced technology can be used to decrease the number of workers needed for food preparation and service.

The tech industry today is abuzz with the potential of AI in healthcare – this discussion is drawing in the interest of very diverse stakeholders, from healthcare providers to drug developers to health insurers and to general public at large. This is not merely hype – the kind of substantial investments pouring in are a proof. According to Accenture, the AI healthcare market is projected to reach $6.6 billion by 2021, expected to result in about $150 billion cost savings annually. Accenture also broke down potential annual benefits for 2026 within the healthcare industry where robot-assisted surgery could easily cut costs by $40 billion while virtual nursing assistants, dosage error reduction, clinical trials and automated image diagnosis could save $20 billion, $16 billion, $13 billion and $3 billion respectively. Gurpreet Singh, a U.S. health services leader at PWC, sees three main areas where major chunks of investment will be heavily focused; digitization, engagement and diagnostics.

All the sessions from Transform 2021 are available on-demand now. Duke Energy's AI journey began because the utility company had a business problem to solve, Duke Energy chief information officer Bonnie Titone told VentureBeat's head of AI content strategy Hari Sivaraman at the Transform 2021 virtual conference on Thursday. Duke Energy was facing some significant challenges, such as the growing issue of climate change and the need to transition to clean energy in order to reach net zero emissions by 2050. Duke Energy is considered an essential service, as it supplies 25 million people with electricity daily, and everything the utility company does revolves around a culture of safety and reliability. The variables together was a catalyst for exploring AI technologies, Titone said, because whatever the company chose to do, it had to support the clean energy transition, deliver value to customers, and find a way for employees to work and improve safety.

Artificial Intelligence has played an increasingly important role within the pharmaceutical space especially with recent restrictions due to COVID-19. The drug development process can be lengthy and costly but many companies have begun implementing AI into their clinical trials to speed up patient on-site visits, test efficacy and bring more drugs to market. As we discussed previously, AI has played an important role in the discovery process. Now let's take a look at AI in clinical trials… PRNewswire reports the global virtual clinical trials market size is expected to reach 11.5 billion USD by 2028 with a compound annual growth rate of 5.7% from 2021 to 2028 according to Grand View Research, Inc. The growth in the virtual clinical trial space is directly related to the need for an increase in patient diversity and an increase in the number of decentralized/virtual trials due to the impact of COVID-19.

AI technologies have also caught the attention of the medical device industry. GE Healthcare, Medtronic and Philips are among top medtechs investing in AI and machine learning, with hopes that the technology will improve diagnosing and treating patients for a wide range of health conditions. Medtronic is focused on AI-aided technologies that would support robotics, navigation, imaging and pre-operative planning for spine surgery. The medtech giant in November acquired French spinal surgery company Medicrea, gaining access to an AI database of more than 5,000 surgical cases. And in December, Philips announced a $2.8 billion deal to buy BioTelemetry, which specializes in remote cardiac diagnostics and monitoring, including wearable heart monitors and AI-based data analytics. The survey includes some of the latest data suggesting a surge in prioritization for such investments, particularly in non-clinical applications as hospitals look to streamline back-end operations to cut costs.

Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach for the partitioning of k-uniform hypergraphs. Most of the existing methods work by reducing the hypergraph to a graph followed by applying standard graph partitioning algorithms. The reduction step restricts the algorithms to capturing only some weighted pairwise interactions and hence loses essential information about the original hypergraph. We overcome this issue by utilizing the tensor-based representation of hypergraphs, which enables us to capture actual super-dyadic interactions. We prove that the hypergraph to graph reduction is a special case of tensor contraction. We extend the notion of minimum ratio-cut and normalized-cut from graphs to hypergraphs and show the relaxed optimization problem is equivalent to tensor eigenvalue decomposition. This novel formulation also enables us to capture different ways of cutting a hyperedge, unlike the existing reduction approaches. We propose a hypergraph partitioning algorithm inspired from spectral graph theory that can accommodate this notion of hyperedge cuts. We also derive a tighter upper bound on the minimum positive eigenvalue of even-order hypergraph Laplacian tensor in terms of its conductance, which is utilized in the partitioning algorithm to approximate the normalized cut. The efficacy of the proposed method is demonstrated numerically on simple hypergraphs. We also show improvement for the min-cut solution on 2-uniform hypergraphs (graphs) over the standard spectral partitioning algorithm.

Currently, it takes a staggering 10-15 years and costs $2.6 billion to develop a new drug, and an astonishing proportion of this is lost in the 90% of candidates that fail. AstraZeneca (AZ) is one of the world's largest Pharmaceutical companies and is incorporating AI in every step of the R&D chain from drug discovery to launch to make this process safer, quicker and less costly. The first step is learning more about diseases. AZ is using knowledge graphs (networks of vast scientific data and the relationship between these) to gather new insights about diseases which would allow for more focused research. In order to learn more about pathology, we must gain a firm understanding of human genomes.

Artificial intelligence (AI) is a beneficial tool for facilities management (FM) teams, that can save money, reduce energy usage, improve productivity, and more. Thanks to AI technology, various aspects of everyday life have evolved, from how we work at the office to how we order groceries or shop online. The Oxford English Dictionary defines AI as, "the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." In this article, we're sharing a few ways AI is already impacting FM teams and organizations as a whole for the better. AI is completely changing the way some organizations cut costs on vital areas of operations like energy, HVAC, security, and other systems.