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 field of biological sciences is becoming increasingly information-intensive and data-rich. For example, the growing availability of DNA sequence data or clinical measurements from humans promises a better understanding of the important questions in biology. However, the complexity and high-dimensionality of these biological data make it difficult to pull out mechanisms from the data. Machine Learning techniques promise to be useful tools for resolving such questions in biology because they provide a mathematical framework to analyze complex and vast biological data. In turn, the unique computational and mathematical challenges posed by biological data may ultimately advance the field of machine learning as well.
GlobalData research has found the top influencers in artificial intelligence based on their performance and engagement online. Using research from GlobalData's Influencer platform, Verdict has named ten of the most influential people in artificial intelligence on Twitter during Q4 2020. Kirk Borne is a principal data scientist and executive advisor at Booz Allen Hamilton, analytics, digital and engineering consulting firm. Borne also serves as an advisory board member of Syntasa, a computer software company, and as an executive committee member of the Astroinformatics and Astrostatistics International Organisations. Ronald Van Loon is a principal analyst and CEO of the Intelligent World, an influencer network that connects people and businesses.
Microsoft's decision to spend $19.7 billion to purchase Nuance Communications took a number of industry analysts by surprise. In justifying the amount, Satya Nadella touted Nuance as a "pioneer" in the field of conversational AI for health care and highlighted the impact its tech would have on establishing Microsoft as a leading vendor in the sector. But the rationale seems to lie beyond trying to own just one industry. Instead, Nuance could fill a critical void that would help Microsoft compete more fiercely against rivals like AWS. To be sure, the health care component is a big deal and gives Microsoft access to a critical sales channel at a time when competing vendors are also pivoting quickly to industry-specific products.
Editor's note: This story led off this week's Future of Learning newsletter, which is delivered free to subscribers' inboxes every other Wednesday with trends and top stories about education innovation. Joanna Smith, founder of an ed-tech company that helps schools curb chronic absenteeism, was thinking about how to pivot her company to provide services in a remote learning setting as many brick and mortar schools transitioned online last year. In April 2020, her company, AllHere, launched several new features to battle problems exacerbated by Covid-19, including an Artificial Intelligence-powered two-way text messaging system, Chatbot, for kids who weren't showing up to class regularly. Chatbot allows teachers to check in with families and provides 24/7 individualized AI support for struggling students. Families can also log on to the platform to get confidential health care referrals or help with computer-related issues.
The reason why we use the cloud so much is the bottom line: Saving money. StormForge, a start-up specializing in reducing cloud waste with machine learning (ML) and artificial intelligence (AI) has found in its recent survey that businesses waste over $17-billion a year on unused or idle cloud resources. Now, it's not that companies have an unrealistic view of what they're going to be spending. Ninety-four percent say they know, at least roughly, what their cloud spend will be each month. The bad news is they also estimate that nearly half of their cloud spend is wasted on unused or idle resources.
If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function. Have you ever thought about what exactly does it mean to use this loss function? The thing is, given the ease of use of today's libraries and frameworks, it is very easy to overlook the true meaning of the loss function used. I was looking for a blog post that would explain the concepts behind binary cross-entropy / log loss in a visually clear and concise manner, so I could show it to my students at Data Science Retreat. Let's start with 10 random points: This is our only feature: x.