If you are a student or professional interested in the latest trends in the computing world, you would have heard of terms like artificial intelligence, data science, machine learning, deep learning, etc. The first article in this series on artificial intelligence explains these terms, and sets the platform for a simple tutorial that will help beginners get started with AI. Today it is absolutely necessary for any student or professional in the field of computer science to learn at least the basics of AI, data science, machine learning and deep learning. However, where does one begin to do so? To answer this question, I have gone through a number of textbooks and tutorials that teach AI. Some start at a theoretical level (a lot of maths), some teach you AI in a language-agnostic way (they don't care whether you know C, C, Java, Python, or some other programming language), and yet others assume you are an expert in linear algebra, probability, statistics, etc. In my opinion, all of them are useful to a great extent. But the important question remains -- where should an absolute beginner interested in AI begin his or her journey? Frankly, there are many fine ways to begin your AI journey.

BRIAN KENNY: Revolutions often have humble origins, a small group with big ideas gathering to plant seeds of disruption. So, it was in the dog days of summer in 1956, when 10 academics gathered on the campus of Dartmouth College to discuss how to make machines use language and form abstractions and concepts to solve the kinds of problems now reserved for humans. The conference led to the founding of a new field of study, artificial intelligence. Six decades hence, we are in the midst of an AI revolution that is already dramatically changing entire sectors like healthcare, transportation, education, banking, and retail. But AI is not without its critics. Elon Musk famously said that, "With artificial intelligence, we're summoning the demon." While Stephen Hawking believed the development of full artificial intelligence could spell the end of the human race. So, whose job is it to make sure that such a vision never comes to pass? Today on Cold Call, we've invited Professor Tsedal Neeley to discuss her case entitled, "Timnit Gebru: Silenced No More on AI Bias and The Harms of Large Language Models." Tsedal Neeley's work focuses on how leaders can scale their organizations by developing and implementing global and digital strategies.

I first saw Sophia when she was talking to Tony Robbins about many things about humanity. Sophia is very beautiful with a unique look. I saw her again (two years before Robbins) when this girl was dating actor Will Smith in the Cayman Islands. Smith offered his lips, but Sophia politely declined. Sophia was born in Hong Kong and then became a citizen of Saudi Arabia.

"In general it is very difficult to build AI that works well for the kinds or kind groups we want it," Ries says in an interview with Fast Company. "There are many cases where you need really specific decisions made about how machine learning should operate." This might be true if there's just one particular group getting trained; but given enough time period machines can figure out what sort people like more, humans will eventually adapt better than any social agents could ever create (not to mention predict when they'll find something useful). Artificial intelligence (AI) is defined as machine learning. Machine Learning is a field in artificial intelligence that involves using computer programs to teach computers how to learn without being explicitly programmed.

AlphaFold, DeepMind's protein structure program, is impressive because it reveals so much fundamental information about living organisms. Proteins are the building blocks of life, after all, and as such they are essential to life and to the development of medicines. Proteins can be drug targets, and they can themselves be drugs. In either case, it is important to know the intricate ways in which they fold into various shapes. Their coils, floppy bits, hidden pockets and sticky patches can control, for example, when a signal is sent between cells or if a process is turned on or off.

This is an interview with Professor Emily Mower Provost that was first published by The Michigan Engineer News Center. Using machine learning to decode the unpredictable world of human emotion might seem like an unusual choice. But in the ambiguity of human expression, U-M computer science and engineering associate professor Emily Mower Provost has discovered a rich trove of data waiting to be analyzed. Mower Provost uses machine learning to help measure emotion, mood, and other aspects of human behavior; for example, she has developed a smartphone app that analyzes the speech of patients with bipolar disorder to track their mood, with the ultimate goal of helping them more effectively manage their health. How do you quantify something as ambiguous as emotion in a field where, traditionally, ambiguity is the enemy?

For decades, scientists have been giddy and citizens have been fearful of the power of computers. In 1965 Herbert Simon, a Nobel laureate in economics and also a winner of the Turing Award (considered "The Nobel Prize of computing"), predicted that "machines will be capable, within 20 years, of doing any work a man can do." His misplaced faith in computers is hardly unique. Sixty-seven years later, we are still waiting for computers to become our slaves and masters. Businesses have spent hundreds of billions of dollars on AI moonshots that have crashed and burned. Watson" was supposed to revolutionize health care and "eradicate cancer."

Debdoot Mukherjee is the Chief Data Scientist and Head of AI at Meesho, the Indian origin social commerce platform at the forefront of the boundaryless workplace model that became a norm in the aftermath of the Covid-19 pandemic. Upon completing his postgraduate degree from IIT-Delhi, Mukherjee began his career in the research division at IBM, where he attained expertise in Information Retrieval and Machine Learning techniques. He then journeyed on to work in impactful roles at companies like Hike, Myntra and ShareChat before leading the AI and data science division at Meesho. In an exclusive interview with Analytics India Magazine, Debdoot Mukherjee opened up about his journey into data science, machine learning and everything AI. AIM: What attracted you to this field?

In artist Miao Ying's animated film Surplus Intelligence, a cockroach falls in love with the artificial intelligence responsible for monitoring her behavior. There's only one problem: The AI, personified as a man with movie-star looks, committed a crime in Walden XII, the quasi-medieval fantasyland where the story is set. He stole the village's power stone, and so the roach sets off to mine bitcoin to save him. Viewers might see in the plot a metaphor for the conflicted relationship some Chinese people have with social credit scoring, which is meant to nudge citizens toward better behavior. Or it could be a nod to the insidious ways social media platforms like Twitter and Facebook condition our behavior and mine us for data.

Robert Logan, and co-authors Alexandre Passos, Sameer Singh and Ming-Wei Chang, won a best new task award at NAACL 2022 (Annual Conference of the North American Chapter of the Association for Computational Linguistics) for their paper FRUIT: Faithfully Reflecting Updated Information in Text. Here, Robert tells us about their methodology, the main contributions of the paper, and ideas for future work. Our paper introduces the new task of faithfully reflecting updated information in text or FRUIT for short. Given an outdated Wikipedia article and new information about the article's subject, the goal is to edit the article's text to be consistent with the new information. Textual knowledge bases such as Wikipedia are essential resources for both humans and machine learning models.