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) …
The University of Surrey has built an artificial intelligence (AI) model that identifies chemical compounds that promote healthy aging -- paving the way towards pharmaceutical innovations that extend a person's lifespan. In a paper published by Nature Communication's Scientific Reports, a team of chemists from Surrey built a machine learning model based on the information from the DrugAge database to predict whether a compound can extend the life of Caenorhabditis elegans -- a translucent worm that shares a similar metabolism to humans. The worm's shorter lifespan gave the researchers the opportunity to see the impact of the chemical compounds. "Ageing is increasingly being recognized as a set of diseases in modern medicine, and we can apply the tools of the digital world, such as AI, to help slow down or protect against aging and age-related diseases. Our study demonstrates the revolutionary ability of AI to aid the identification of compounds with anti-aging properties."
This is a topic that I had several discussions on with colleagues of mine as well as friends. With co-pilot, and directions of autoML, 1-liner libraries to run algorithms, what should be the value of data-scientist? TLDR: Personally, I feel that the value of a data-scientist should not be the ability to "write" just the latest machine learning API, but more towards critical thinking skills, problem solving abilities (generic), problem formulation as well as communication. I will try to touch on them more and relate them to personal experiences.
A young computer scientist who grew up in Mississippi is focusing her efforts on fairness and identifying biases in technology. Though she's working in Atlanta as an artificial intelligence researcher for Amazon, she's reinvesting much of her earnings towards the development of a multi-million dollar innovation center that's set to transform her native downtown Jackson. Dr. Nashlie Sephus is also CEO of The Bean Path -- a non-profit that works to bridge the "tech gap" in communities where access to technical expertise, computer coding and other resources are limited. She speaks candidly with host Eddie Robinson about her experiences in closing commercial real estate deals in the Deep South and how she's worked in a field of study where there's not many Black women with PhDs. Fill out the form below to subscribe our new daily editorial newsletter from the HPM Newsroom.
For oil and gas companies looking at drilling wells in a new field, the issue becomes one of return vs. cost. The goal is simple enough: install the fewest number of wells that will draw them the most oil or gas from the underground reservoirs for the longest amount of time. The more wells installed, the higher the cost and the larger the impact on the environment. However, finding the right well placements quickly becomes a highly complex math problem. Too few wells sited in the wrong places leaves a lot of resources in the ground.
Using satellites, drones and artificial intelligence, emerging technology is changing the way firefighting agencies and governments battle the ever-increasing threat of wildfires as hundreds of thousands of acres burn across the western United States. New programs are being developed by startups and research institutions to predict fire behavior, monitor drought and even detect fires when they first start. As climate change continues to increase the intensity and frequency of wildfires, these breakthroughs offer at least one tool in the growing arsenal of prevention and suppression strategies. "This is not to replace firefighting on the ground," said Ilkay Altintas, a computer scientist with the University of California, San Diego, who developed a fire map for the region. "The more science and data we can give firefighters and the public, the quicker we'll have solutions to combat and mitigate wildfires."
"We're excited about it," Peter Colis, Ethos CEO and co-founder, said of the SoftBank investment. "It's more capital to fuel our mission of protecting families." Ethos plans to use the funds to build out its engineering and products team, as well as for research and development. Employees currently number about 200 people and are expected to jump to 350 to 400 by the end of the year, he said. SoftBank's investment is coming from its $30 billion Vision Fund 2. The pool typically focuses on companies that use artificial intelligence like Carro, the Singapore online car marketplace; DiDi, the Uber of China; and eToro, the Israeli online stock brokerage.
Every possible organization that one can think of relies on data to achieve the set objectives. On that note, having access to data that isn't smart enough to get goals accomplished poses a hurdle. It is thus important to have data that is transformed in a manner that can cater to the needs and objectives of the organization. With most organizations relying on Artificial Intelligence (AI) and machine learning, the necessity of dealing with the right data is all the more important for the sole reason that the models employed aim at obtaining meaningful insights. No wonder data is vast and one shouldn't ideally fall short of it while aiming at the objectives.
Let's have an insight into the democratization of AI and its pros and cons Internet is everywhere, anyone from anywhere could access it and learn many things indeed. The same applies to Artificial Intelligence (AI) as well. Anyone with access to the internet could learn and explore the realms of AI without depending on an external factor like a course or maybe a degree. Anyone who has a spark to learn AI in and out could do it just with the readily available sources. This is the exact concept of the Democratization of Artificial Intelligence.
In the United States, an estimated 114,000 people were waiting for organ transplants, and only 30% of those got their organs on time in 2019. According to Kaiser Health News and Reveal from the Center of Investigative Reporting, nearly 170 organs could not be transplanted. Almost 370 endured near misses with delays of two hours or more because of transportation problems. According to the American Transplant Foundation, 20 people die each day because they do not receive their lifesaving organs in time, making the number of annual deaths greater than 40,000. Patti Niles is CEO of Southwest Transplant Alliance, a non-profit organ procurement organization.