Machine learning technology has a range of applications in a range of industries in professions. For example, machine learning technology has become a popular fixture in the healthcare field. The ability to feed data into a machine and have an algorithm that can interpret the data, machine learning offers doctors and clinicians the ability to make diagnosis or spot information on an imaging scan, for example, that might not have been visible before. The general idea is that machine learning can take large quantities of data to solve problems that might be more difficult for humans to do alone. But what if machine learning were so good it could actually predict the future.

With Artificial Intelligence (AI) and Machine Learning (ML), the healthcare industry is continuing to undergo a transformation. Valued at US$10.4bn last year, the global artificial intelligence (AI) in healthcare market is expected to continue to grow at a compound annual growth rate (CAGR) of 38.4% from 2022 to 2030. And with breakthroughs such as a report that AI could be used to identify conditions such as Parkinson's disease years before the appearance of physical symptoms, there appears to be a healthy future for the relationship between technology and medicine. Researchers at MIT have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns while they are sleeping. Parkinson's disease is hard to diagnose, researchers say, because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which can often appear several years after the disease onset.

Machine learning is Competence without Comprehension as famously noted by Dan Dennett, the pre-eminent philosopher of our times. There are two aspects to Machine Learning (ML) comprehension . Artificial General Intelligence (AGI) hopes to infuse ML with comprehension. The other less lofty aspect is that WE would like to comprehend how ML reaches its decisions and predictions! To accomplish the latter, we need Explainable ML explanation is the evidence of comprehension . . .

The main objective of this work is to use machine-learning (ML) algorithms to develop a powerful model to predict well-integrity (WI) risk categories of gas-lifted wells. The model described in the complete paper can predict well-risk level and provide a unique method to convert associated failure risk of each element in the well envelope into tangible values. The predictive model, which predicts the risk status of wells and classifies their integrity level into five categories rather than three broad-range categories, as in qualitative risk classification. The five categories are Category 1, which is too risky Category 2, which is still too risky but less so than Category 1 Category 3, which is medium risk but can be elevated if additional barrier failures occur Category 4, which is low risk but features some impaired barriers Category 5, which is the lowest in risk The failure model, which identifies whether the well is considered to be in failure mode. In addition, the model can identify wells that require prompt mitigation.

It is now abundantly evident in the post-Covid era that things will no longer be like before and adoption of technology and embracing automation is a must for the survival in the new normal world. We are also observing that there is hardly a day in Bangladesh when a politician, business leader or a civil servant does not talk about the fourth industrial revolution and the effects of artificial intelligence on our economy. Prime Minister Sheikh Hasina also said on December 11 in 2021 that the government is preparing the country to take advantage of the potential presented by the fourth industrial revolution (4IR) in order to boost economic growth to the desired level. Now the debate that has been going for so long whether artificial intelligence will take away jobs from humans or will it create more jobs? When the first industrial revolution used water and steam power to mechanise production, people thought that people will lose job and machine will replace human, but actually it did not happen.

With traditional device scaling and the forces of Moore's Law coming to a halt, modern approaches to computing are beginning to reach their limits. Instead, many developers have turned their sights to promising new computing methods, including quantum computing and neuromorphic computing. While both of these technologies hold exciting potential, there is still significant work to be done in each field. Today, Intel is pushing the state of both quantum and neuromorphic computing with new releases at Intel Innovation 2022. All About Circuits heard from Anne Matsuura, director of the quantum and molecular technologies at Intel, and Mike Davies, director of Intel's neuromorphic computing lab, to hear about the new releases firsthand.

After nearly two years of supply issues, you can kinda sorta get a PlayStation 5 or Xbox Series X now, as long as you stay on top of stock alerts. That means this holiday season is the time to get a baller new 4K TV that makes the most of each console's capabilities. The only problem is that not every 4K TV has the right feature set to maximize the potential of the PS5 or Xbox Series X. There's one feature in particular that some cheaper 4K TVs might not have: variable refresh rate, or VRR. This is super important to have if you want the most tricked-out gaming experience both consoles can offer.

For a decade now, many of the most impressive artificial intelligence systems have been taught using a huge inventory of labeled data. An image might be labeled "tabby cat" or "tiger cat," for example, to "train" an artificial neural network to correctly distinguish a tabby from a tiger. The strategy has been both spectacularly successful and woefully deficient. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Such "supervised" training requires data laboriously labeled by humans, and the neural networks often take shortcuts, learning to associate the labels with minimal and sometimes superficial information.

Intellegens has announced the latest release of its machine learning software, focused on key practical tasks to accelerate innovation for chemicals, materials, and manufacturing. Alchemite applies an Artificial Intelligence (AI) method developed at the University of Cambridge to optimise products and processes, reduce experimental workloads, and enable faster R&D progress. Ben Pellegrini, CEO at Intellegens comments: "We work closely with our chemical, materials, and manufacturing customers to focus our development work. Often, it's the small details that really make the software work in an industrial R&D environment. We're delighted that more organisations now benefit from Alchemite. Significant customer agreements so far this year have included a major steel manufacturer, world-leading speciality chemicals providers, additive manufacturing specialists, and a top global food producer."

Artificial intelligence (AI) is a revolutionary technology disrupting virtually every sector of the economy, from manufacturing and retail to sports and entertainment. The average consumer may not know that AI is all around them. Companies can use AI to curate users' social media feeds, make new drug discoveries, power digital voice assistants, and allow smartphone owners to unlock their phones with facial recognition. One specific type of AI – generative AI – is an advancement in AI that holds great promise. Take a deeper look at generative AI, examples of its common applications, and how it can drive growth for businesses of all types and sizes.