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) …
Ben Horowitz resoundingly falls in the category of "needing no introduction": a highly successful entrepreneur who navigated a perilous situation with his business (Loudcloud, which became Opsware) to a $1.65B acquisition by HP, he's also the founder of premier Silicon Valley venture capital firm Andreessen Horowitz (aka "a16z"), and the best selling author of two books: "The Hard Thing About Hard Things" and the newly-released "What You Do Is Who You Are". It was a special treat to host Ben for a fireside chat at the most recent most recent edition of Data Driven NYC – a great evening that included two other terrific speakers: Amr Adwallah, now VP of Developer Relations at Google Cloud, and previously co-founder and CTO at Cloudera (NYSE: CLDR) and Michael James, co-founder of AI chip Cerebras. We spent a good hour with Ben and covered a bunch of topics, loosely organized in two parts, first AI and data, and then culture an his new book. Below are two videos covering each part, as well as a FULL TRANSCRIPT for anyone who prefers to read.
Machine learning is increasingly used across fields to derive insights from data, which further our understanding of the world and help us anticipate the future. The performance of predictive modeling is dependent on the amount and quality of available data. In practice, we rely on human experts to perform certain tasks and on machine learning for others. However, the optimal learning strategy may involve combining the complementary strengths of humans and machines. We present expert-augmented machine learning, an automated way to automatically extract problem-specific human expert knowledge and integrate it with machine learning to build robust, dependable, and data-efficient predictive models. Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert.
The European Commission released long-awaited position papers Wednesday on several key digital issues, including how to treat the continent's digital data and how best to regulate artificial intelligence. Why it matters: Europe has traditionally trailed the U.S. in creating giant tech companies that gobble up consumer data, but it has led in issuing rules and policies to govern such practices. The European Data Strategy and the AI recommendations have themes will be familiar. What they're saying: Not surprisingly, many trade groups released statements praising the goals of the proposals, while urging restraint in regulation. Meanwhile: Cornell business professor Thomas Jungbauer argues the proposals aren't what's needed to help Europe catch up.
Chatbots and other artificial intelligence-assisted customer service tools continue to grow exponentially as retailers look to improve efficiencies. The benefits of chatbots and electronic systems for businesses are relatively straightforward: less time spent monitoring communication channels, less expensive customer service solutions, and even the ability to grow by converting potential customers. Some analysts believe chatbots -- and the evolving capabilities of AI -- could revolutionize industries and propel companies to new levels of success. One analysis found that the global chatbot market is expected to reach more than $10 billion by 2026. So that's the business side; it's pretty clear cut why organizations deploy automated solutions.
A machine learning algorithm reveals how to quickly charge batteries without damaging them. Research Highlight: The'silent' language of mice is decoded at last; Research Article: Gu et al. A new device produces electricity using water in the air. Coronavirus outbreak updates, the global push to conserve biodiversity, and radar reveals secrets in an ancient Egyptian tomb. News: Coronavirus: latest news on spreading infection; News: China takes centre stage in global biodiversity push; News: Is this Nefertiti's tomb?
Most senior leaders would agree that adapting in real time to customer and market needs is vital for achieving their visions and goals. And to develop this capability, they'd also likely agree they need data and artificial intelligence (AI). The examples are all around us. The city of Chicago uses 12 variables, including high daily temperatures, to prioritize which of the city's 7,000 "high-risk" restaurants it should send its 35 food inspectors to. The AI solution found violations a week earlier than they otherwise would have.
Garry Kasparov is perhaps the greatest chess player in history. For almost two decades after becoming world champion in 1985, he dominated the game with a ferocious style of play and an equally ferocious swagger. Outside the chess world, however, Kasparov is best known for losing to a machine. In 1997, at the height of his powers, Kasparov was crushed and cowed by an IBM supercomputer called Deep Blue. The loss sent shock waves across the world, and seemed to herald a new era of machine mastery over man.
Automation has made it much easier for businesses to operate, especially by taking over repetitive, menial tasks. But as new advances are going live, the threat of automation to the job market seems like it might be a reality. Whether for good or ill, automation is currently an established part of business operations. As they become more sophisticated, automation systems combined with other new technology such as artificial intelligence and internet of things could signal a massive shift in how employees see their jobs and their responsibilities. Below, 15 professionals from Forbes Coaches Council share their predictions about what automation will do to the job market as it continues to reach new industries.
A company that wants to become AI-powered and build solutions for real world problems has to overcome several challenges along the way. One of the most common obstacles is to get access to AI experts and data scientists that can help to translate a problem into a deployable AI solution or prototype. In addition, many organizations have little or no data to begin with. And even if data is plentyful, the question remains, how to leverage the raw data to solve problems or gain valuable insights. If an organization made the step to develop an AI system, the next wave of challenges is just around the corner.
Today, artificial Intelligence (AI) helps you shop, provides suggestions on what music to listen to and what shows to watch, connects you with friends on social media and even drives your car. As more companies focus their efforts on AI-based solutions, 2020 is shaping up to be a turning point as we begin to witness the third wave of AI -- when AI systems not only not learn and reason as they encounter new tasks and situations, but have the ability to explain their decision making. The first wave of AI focused on enabling reasoning over narrowly defined problems, but lacked any learning capability and poorly handled uncertainty. Financial products like Turbotax and Quickbooks, for example, are able to take information from a situation where rules have previously been defined and work through it to achieve a desired outcome. However, they are unable to operate beyond the previously defined rules.