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) …
Editor's note: Sourav Mazumder is a speaker for ODSC West 2021. Be sure to check out his talk, "Operationalization of Models Developed and Deployed in Heterogeneous Platforms," for more info on trustworthy AI there. Artificial intelligence (AI) is already having a significant impact on the development of humanity, already. For enterprises, the use of AI is not an option anymore. However, the core of AI relies on the use of data samples/examples to train a system/machine using algorithms so that it can behave intelligently like a human.
The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Women in the AI field are making research breakthroughs, launching exciting companies, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. And that's why we created the VentureBeat Women in AI Awards -- to emphasize the importance of their voices, work, and experiences, and to shine a light on some of these leaders. We first announced the six winners at Transform 2021 in July, and ever since, we've been catching up with each of them for deeper discussions around their work and emerging challenges in the field. Our conversations have touched on everything from regulation and dealing with messy real world data to how to approach AI more responsibly.
Note that there is also a repository of this article with all the resources clearly identified for you to follow in order as well. In my opinion, the best way to start learning anything is with short YouTube video introductions. This field is no exception. There are thousands of amazing videos and playlists that teach important machine learning concepts for free on this platform, and you should definitely take advantage of them. Here, I list a few of the best videos I found that will give you a great first introduction to the terms you need to know to get started in the field.
AI researchers often say good machine learning is really more art than science. The same could be said for effective public relations. Selecting the right words to strike a positive tone or reframe the conversation about AI is a delicate task: done well, it can strengthen one's brand image, but done poorly, it can trigger an even greater backlash. The tech giants would know. Over the last few years, they've had to learn this art quickly as they've faced increasing public distrust of their actions and intensifying criticism about their AI research and technologies.
This past year has seen a significant blossoming of discussions on the ethics of AI. In working groups and meetings spanning IEEE, ACM, U.N. and the World Economic Forum as well as a handful of governmental advisory committees, more intimate breakout sessions afford an opportunity to observe how we, as robotics and AI researchers, communicate our own relationship to ethics within a field teeming with possibilities of both benefit and harm. Unfortunately, many of these opportunities fail to realize authentic forward progress during discussions that repeat similar memes. Three common myths pervade such discussions, frequently stifling any synthesis: education is not needed; external regulation is undesirable; and technological optimism provides justifiable hope. The underlying good news is that discourse and curricular experimentation are now occurring at scales that were unmatched in the recent past.
While ethical artificial intelligence (AI) and analytics adoption can add about US$2 trillion (S$2.7 trillion) of value each year to the global banking and insurance industry, a Temasek survey showed that only 13 per cent of the sector uses AI solutions across the bulk of its processes. Financial firms in select markets including Singapore were polled in the survey. About 31 per cent of the companies are still only dipping their toes in AI-driven solutions, while more than half of the respondents fell somewhere in the middle - using AI in some areas, but not harnessing its full potential. The findings mean that, while almost all financial services companies use AI in their processes in some way, they differ in the extent of AI deployment, with an overwhelming 93 per cent of the companies demanding that AI solutions should be trustworthy, said Temasek. The study was conducted by Temasek in March this year, on an anonymous basis, with 39 decision-makers from banks and insurance companies in the United States, Europe, Singapore and Hong Kong.
I made a playlist of 11 short videos (most are 6-13 mins long) on Ethics in Machine Learning. This is from my ethics lecture in Practical Deep Learning for Coders v4. I thought these short videos would be easier to watch, share, or skip around. What are Ethics and Why do they Matter? Machine Learning Edition: Through 3 key case studies, I cover how people can be harmed by machine learning gone wrong, why we as machine learning practitioners should care, and what tech ethics are.
All the sessions from Transform 2021 are available on-demand now. Women in the AI field are making research breakthroughs, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. We created the VentureBeat Women in AI Awards to emphasize the importance of their voices, work, and experience and to shine a light on some of these leaders. In this series, publishing Fridays, we're diving deeper into conversations with this year's winners, whom we honored recently at Transform 2021. Check out last week's interview with the winner of our AI research award.
The proliferation of artificial intelligence and algorithmic decision-making has helped shape myriad aspects of our society: From facial recognition to deep fake technology to criminal justice and health care, their applications are seemingly endless. Across these contexts, the story of applied algorithmic decision-making is one of both promise and peril. Given the novelty, scale, and opacity involved in many applications of these technologies, the stakes are often incredibly high. This is the introduction to FTC Commissioner Rebecca Kelly Slaughter's whitepaper: Algorithms and Economic Justice: A Taxonomy of Harms and a Path Forward for the Federal Trade Commission. If you have been keeping up with data-driven and algorithmic decision-making, analytics, machine learning, AI, and their applications, you can tell it's spot on.
We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.