If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Mind Foundry has been a pioneer in the development and use of'humble and honest' algorithms from the very beginning of its applications development. As Davide Zilli, Client Services Director at Mind Foundry explains, 'baked in' transparency and explainability will be vital in winning the fight against biased algorithms and inspiring greater trust in AI and ML solutions. Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons – and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023.
Given the intrinsic complex and dynamic nature of ML, the possibility of failure is not a surprise. There are many reasons why this can happen. One of them is bias in the training data and method (e.g. Another reason is that the ultimate scope of the ML is not well defined and transparent and does not match any specific business requirements. Further issues are linked to the machine learning techniques which are not able to inform us when the information is not clear, or they cannot effectively learn from the data.
"With more board configurations than there are atoms in the observable universe, the ancient Chinese game of'Go' has long been considered a grand challenge for artificial intelligence. On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined the Google DeepMind Challenge Match. Hundreds of millions of people around the world watched as a legendary Go master took on an unproven AI challenger for the first time in history. Directed by Greg Kohs with an original score by Academy Award nominee, Hauschka, AlphaGo chronicles a journey from the halls of Cambridge, through the backstreets of Bordeaux, past the coding terminals of DeepMind in London, and, ultimately, to the seven-day tournament in Seoul. As the drama unfolds, more questions emerge: What can artificial intelligence reveal about a 3000-year-old game? What can it teach us about humanity?"
Of the many ingredients that go into quality healthcare, comprehensive patient data is close to the top of the list. No one knows this more than Mayur Saxena, CEO and founder of Droice Labs. Saxena created his startup while he was pursuing his doctorate degree at Columbia University, and working at healthcare company conducting clinical trials on new medication. He's energized by the plethora of opportunities to improve healthcare using artificial intelligence (AI) and machine learning. Mayur Saxena, CEO and founder of Droice Labs, is energized by the plethora of opportunities to improve healthcare using artificial intelligence (AI) and machine learning.
The number of AI-related healthtech transactions has doubled every six months since the second half of 2017, reaching 10% of all deals by the end of 2019, according to a report from Hampleton Partners. The international technology mergers and acquisitions advisors' report says that overall healthtech transactions have seen a healthy rebound after dropping significantly in the second half of 2016 and the first half of 2017. Volumes increased by 14% compared to the first half of 2019 and 27% compared to the same period last year. The majority of recent disclosed healthtech transactions were aimed at process efficiency, primarily tackling mounting cost pressures and inefficiencies across the healthcare industry but also tackling new challenges. Jonathan Simnett, director, Hampleton Partners, said: "The phenomenal progress being made in healthcare technology, including genomics, is generating exabytes of data that need analysis, storage and security. That puts the spotlight on AI and machine learning companies which can comb through this massive data pool to extract what's needed to deliver the tailored therapies, drug discovery and care delivery that the sector wants to achieve. "The number of AI targets is rising exponentially as the early movers and pioneers in this space are maturing and becoming ripe for sale.
In a landmark discovery, a machine learning based AI has been used to uncover powerful new antibiotics that can kill resistant bacterial strains. Around 46,000 people dying each year in the UK alone from sepsis, with many cases being caused by antibiotic resistant bacteria that don't respond to treatment. The World Health Organisation has has identified several high-priority target pathogens that new antibiotics should target, but development of just one new drug can take years of research and millions of pounds in funding. Researchers at the Massachusetts Institute of Technology (MIT) in the US have now successfully used an AI to discover new antibiotic drugs for the first time. The team trained the AI on a data set of known antimicrobial molecules and then set it loose on a vast pharmaceutical database to assess the potential of each drug as an antibiotic.
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AI researchers continue to develop larger and more complicated models that can tackle more complicated language-related tasks. In the past year, we've seen the release of state-of-the-art language models such as OpenAI's GPT-2 and Google's Meena. While we're still pretty far from developing AI that can truly understand human language, practical uses will emerge from continued advances in natural language processing. AI will do the legwork, gathering import data and highlighting trends in text data, making it easier and less costly for insurers to piece that information together and address their clients' needs.