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) …
Natural language processing has garnered interest in helping people interact with computer systems to make sense and meaning of the world. In the area of visual analytics, natural language has been shown to help improve the overall cognition of visualization tasks. In this latest Data Science Central webinar, Vidya will discuss how natural language can be leveraged in various aspects of the analytical workflow ranging from smarter data transformations, visual encodings, autocompletion to supporting analytical intent. More recently, chatbot systems have garnered interest as conversational interfaces for a variety of tasks. Machine learning approaches have proven to be promising for approximating the heuristics and conversational cues for continuous learning in a chatbot interface.
From startups to enterprises racing to get new products launched, AI and machine learning (ML) are making solid contributions to accelerating new product development. There are 15,400 job positions for DevOps and product development engineers with AI and machine learning today on Indeed, LinkedIn and Monster combined. Capgemini predicts the size of the connected products market will range between $519B to $685B this year with AI and ML-enabled services revenue models becoming commonplace. Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout.
WellAI data scientists Daniel Satchkov and Sergei Polevikov will present their most recent research entitled "Reading 25 Million Studies in Seconds: Implications for Fighting COVID-19 and Managing a Portfolio" at a free webinar on August 25, 2020. The webinar will take place from 12pm to 1pm EST, and is jointly organized by the Society of Quantitative Analysts (SQA) and WellAI. Discussion will be partly based on a study "Artificial Intelligence-powered search tools and resources in the fight against COVID-19″ published in the Journal of the International Federation of Clinical Chemistry and Laboratory Medicine in June 2020, and is currently available through the PubMed database of the National Institutes of Health (NIH). Sergei Polevikov, CEO of WellAI and a board director at SQA, explained: "We wanted to share our unique experience as we believe our work is relevant to both medical researchers and finance professionals. WellAI data scientists had built a free COVID-19 analytical tool for medical researchers around the world in early April 2020, to help fight the pandemic.
Recently, a team of researchers from UC Berkeley and Adobe Research proposed a new machine learning model known as Swapping Autoencoder, which has the capability to perform image manipulation. The key idea of this research is to encode a picture into 2 independent components and then enforce that any swapped combination maps to a realistic image. Deep generative models such as GANs or Generative Adversarial Networks and Variational Autoencoders (VAEs) have gained much traction by the researchers over the years. According to the researchers, deep generative models have become a popular technique when it comes to producing realistic images from randomly sampled data. However, such deep generative models face various challenges when used for a controllable manipulation of existing images.
A coalition of AI groups is forming to produce a comprehensive data source on the coronavirus pandemic for policymakers and health care leaders. Why it matters: A torrent of data about COVID-19 is being produced, but unless it can be organized in an accessible format, it will do little good. The new initiative aims to use machine learning and human expertise to produce meaningful insights for an unprecedented situation. Driving the news: Members of the newly formed Collective and Augmented Intelligence Against COVID-19 (CAIAC) announced today include the Future Society, a non-profit think tank from the Harvard Kennedy School of Government, as well as the Stanford Institute for Human-Centered Artificial Intelligence and representatives from UN agencies. What they're saying: "With COVID-19 we realized there are tons of data available, but there was little global coordination on how to share it," says Cyrus Hodes, chair of the AI Initiative at the Future Society and a member of the CAIAC steering committee.
Artificial intelligence (AI) presents an opportunity to transform how we allocate credit and risk, and to create fairer, more inclusive systems. AI's ability to avoid the traditional credit reporting and scoring system that helps perpetuate existing bias makes it a rare, if not unique, opportunity to alter the status quo. However, AI can easily go in the other direction to exacerbate existing bias, creating cycles that reinforce biased credit allocation while making discrimination in lending even harder to find. Will we unlock the positive, worsen the negative, or maintain the status quo by embracing new technology? This paper proposes a framework to evaluate the impact of AI in consumer lending. The goal is to incorporate new data and harness AI to expand credit to consumers who need it on better terms than are currently provided. It builds on our existing system's dual goals of pricing financial services based on the true risk the individual consumer poses while aiming to prevent discrimination (e.g., race, gender, DNA, marital status, etc.).
Q-learning is a model-free reinforcement learning algorithm to learn a policy telling an agent what action to take under what circumstances. It does not require a model of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. "Q" names the function that returns the reward used to provide the reinforcement and can be said to stand for the "quality" of an action taken in a given state.
One of the biggest challenges for language-processing artificial intelligence is figuring out the underlying meaning of slang, colloquialisms, and intentional misspellings. In order to help those hapless machines out, a team of mathematicians from the University of Vermont started to analyze how young people deliberately stretch words when they type. For instance, they've quantified the semantic difference between stretched words like "hahaha" and "haaahaha" in hopes that future AI algorithms can learn to understand us in the informal ways we actually communicate online. In their research, published Wednesday in the journal PLOS One, the team analyzed the so-called "stretchable words" that appeared in 100 billion tweets posted over the past eight years. They then came up with two measurements: balance and stretch.
Nobody knows for sure what the post-COVID world will look like. But you can certainly bet it's going to be different. The pandemic has already pummeled the global economy and exposed weaknesses in supply chains and vintage software systems. But it has also accelerated automated delivery of goods and services, autonomous customer interactions and forced companies once skeptical of work-from-home culture to embrace it more than ever before. "And, yet, for many executives," says Muthulakshmi (Lakshmi) N, Global Head, Intelligent Process Automation and AI at Tata Consultancy Services (TCS), "a major roadblock to scaling automation is the misconception that aggressive, holistic automation will produce widespread job loss. But this view fails to imagine the new types of jobs that will be created when automation frees employees from work that can be done faster, better, and less expensively by artificial intelligence (AI)."