Savvy investors will want to check out stocks that capitalize on artificial intelligence -- using computers to simulate human thinking, reasoning and problem solving. Companies that use AI technology often do so by using software to process huge amounts of data. As the pandemic of 2020 forced lockdowns, many businesses took advantage of AI to interact with the public, as face-to-face contact was limited. That trend seems likely to continue. In a November market forecast, Gartner Inc. predicted the market for AI would grow 21.3%, to $62.5 billion, in 2022.

Picture this: A newborn baby gets thrown into a deep-end pool and needs to learn how to swim for survival. This newborn baby will go through the method of trial and error to improve itself. It will learn from its mistakes and improve its accuracy over time. An artificial neural network (ANN) is a computing system that can learn on its own. It creates an adaptive system that computers use to learn from their mistakes and improve continuously.

While'data' might be the new oil, the'dataset' is the refined gasoline that powers every Machine Learning (ML) and AI operation. These datasets are used to boost signal, accuracy, precision, profit/loss, Sortino or Sharpe ratios in the financial markets and biosciences industries. The following is a transcript of a recent AMA hosted by ABT Crypto Academy on Telegram with the founder of Vectorspace AI, Kasian Franks. We're extremely privileged to be joined by Kasian Franks the CEO of Vectorspace, it's only right we start off with a brief introduction -- can you tell us what exactly Vectorspace is and how the idea came about? We got our start in Life Sciences, now most refer to it as Biosciences at Genentech and Lawrence Berkeley National Lab https://www.lbl.gov There we were tasked with creating a system to identify hidden relationships between genes, drugs and diseases connected to breast cancer, chromosomal radiation damage and extending human lifespan for the purpose of deep space travel. We wrote a paper with Micheal I. Jordan, teacher of Andrew Ng, who developed the first AI for Google and China's Baidu. "The statistical modeling of biomedical corpora could yield integrated, coarse-to-fine views of biological phenomena that complement discoveries made f…" Human genes are like stocks or cryptos. Our technology is based on datasets.

A newly developed artificial intelligence (AI) model is able to spot breast cancer better than a clinician, new research has suggested. Google DeepMind, in partnership with Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, has developed the model which can spot cancer in breast screening mammograms in a bid to improve health outcomes and ease pressure on overstretched radiology services. Initial findings, published by the technology giant in the journal Nature, suggest the AI can identify the disease with greater accuracy, fewer false positives and fewer false negatives. The model, trained on de-identified data of 76,000 women in the UK and more than 15,000 women in the US, reportedly lowered false positive results by 1.2% and false negatives by 2.7% in the UK, but is yet to be tested in clinical studies. When tested, the AI system processed only the latest available mammogram of a patient, whereas clinicians had access to patient histories and prior mammograms to make an informed screening decision.

For data-complex and risk-adverse industries like insurance, being able to access data locked away in file stores and data lakes is critical for effective decision making. Data collection and analysis is at the heart of insurance business processes. Real-time data extraction enables insurers to automate and standardize time-consuming labor-intensive processes. With insurers being under pressure to deliver a better customer experience, they are being forced to examine existing processes and adopt new methods of doing business. But given the plethora of technology available, it can be difficult to understand what it is and how to use it.

The University of Sydney's Westmead Applied Research Centre has been awarded a $1 million grant from technology giant Google to research and develop ways artificial intelligence could be used in digital tools to reduce the risk of heart attacks. Westmead Applied Research Centre will utilise the Google AI Impact challenge grant to create customised digital health tools that enable clinicians and health services to support more people to prevent cardiovascular disease. By combining clinical and consumer-derived data, such as from mobile phone apps and wearables, the program will offer tailored advice using machine learning to assess participants who have been to the hospital with chest pain, harnessing their digital footprint to reduce the risk of a heart attack. Dr. Harry Klimis, a cardiologist and researcher at WARC, explained to HealthcareITNews that modifiable risk factors account for more than 90 percent of the risk of heart attack worldwide. These include abnormal cholesterol, hypertension, smoking, excess alcohol consumption, physical inactivity, diabetes, obesity, psychosocial factors and diet.

There seems to be a general consensus that AI will by no means make the work done by financial professionals redundant since results produced by AI models still require human interpretation, says the writer. Artificial intelligence (AI) is nothing new. It has been around since the 50s. It is only now that we possess the processing power to utilise it properly. It is already embedded in many people's lives.

Adobe researchers have developed an AI tool that could make spotting'deepfakes' a whole lot easier. The tool is able to detect edits to images, such as those that would potentially go unnoticed to the naked eye, especially in doctored deepfake videos. It comes as deepfake videos, which use deep learning to digitally splice fake audio onto the mouth of someone talking, continue to be on the rise. Adobe researchers have developed an AI tool that could make it easier to spot'deepfakes'. Deepfakes are so named because they utilise deep learning, a form of artificial intelligence, to create fake videos. They are made by feeding a computer an algorithm, or set of instructions, as well as lots of images and audio of the target person.

As artificial intelligence continues to evolve, algorithms will be capable of performing clinical tasks -- like diagnosing disease -- much faster and with greater accuracy than any human physician. At that point, the argument goes, we physicians will be out of work. I've now encountered this opinion throughout Silicon Valley and have had a number of students approach me with concern. Should medical professionals be worried? I can trace some of the anxiety back to last year, when researchers at Stanford Medicine announced they had developed an algorithm that, in just a matter of weeks, was trained to interpret chest x-ray images to diagnose more than a dozen medical conditions.

Greater performance accuracy is implicit in the evolution of robot intelligence, says Fanuc Robotics' Dick Johnson, general manager, material handling. "For example, there is a trend to offer tools that will increase the accuracy of robots by compensating for variations in the manufacturing process. That promises to both decrease robot programming time and also make possible new robot applications."