In December, the University of Texas at Austin's computer science department announced that it would stop using a machine-learning system to evaluate applicants for its Ph.D. program due to concerns that encoded bias may exacerbate existing inequities in the program and in the field in general. This move toward more inclusive admissions practices is a rare (and welcome) exception to a worrying trend in education: Colleges, standardized test providers, consulting companies, and other educational service providers are increasingly adopting predatory, discriminatory, and outright exclusionary student data practices. Student data has long been used as a college recruiting and admissions tool. In 1972, College Board, the company that owns the PSAT, the SAT, and the AP Exams, created its Student Search Service and began licensing student names and data profiles to colleges (hence the college catalogs that fill the mail boxes of high school students who have taken the exams). Today, College Board licenses millions of student data profiles every year for 47 cents per examinee.
Typically the presenters at a CES press conference don't get a lot of attention. Wearing a pink hooded sweatshirt with the phrase "Stay punk forever," Reah Keem was among presenters highlighting some of the offerings from LG, ranging from appliances to personal technology. LG describes her as a "virtual composer and DJ made even more human through deep learning technology." Keem was there to introduce the LG CLOi robot, which can disinfect high-traffic areas using ultraviolet light. You can watch Reah make her debut during LG's press conference Monday morning, at roughly the 22-minute mark.
Among the things I have not missed since entering middle age is the sensation of being an absolute beginner. It has been decades since I've sat in a classroom in a gathering cloud of incomprehension (Algebra 2, tenth grade) or sincerely tried, lesson after lesson, to acquire a skill that was clearly not destined to play a large role in my life (modern dance, twelfth grade). Learning to ride a bicycle in my early thirties was an exception--a little mortifying when my husband had to run alongside the bike, as you would with a child--but ultimately rewarding. Less so was the time when a group of Japanese schoolchildren tried to teach me origami at a public event where I was the guest of honor--I'll never forget their sombre puzzlement as my clumsy fingers mutilated yet another paper crane. Like Tom Vanderbilt, a journalist and the author of "Beginners: The Joy and Transformative Power of Lifelong Learning" (Knopf), I learn new facts all the time but new skills seldom.
IJCAI-PRICAI2020, the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence starts today and will run until 15 January. Find out what's happening during the event. The conference schedule is here and includes tutorials, workshops, invited talks and technical sessions. There are also competitions, early career spotlight talks, panel discussions and social events. There will be eight invited talks on a wide variety of topics.
If you follow the news on artificial intelligence, you'll find two diverging threads. The media and cinema often portray AI with human-like capabilities, mass unemployment, and a possible robot apocalypse. Scientific conferences, on the other hand, discuss progress toward artificial general intelligence while acknowledging that current AI is weak and incapable of many of the basic functions of the human mind. But regardless of where they stand in comparison to human intelligence, today's AI algorithms have already become a defining component for many sectors, including health care, finance, manufacturing, transportation, and many more. And very soon "no field of human endeavor will remain independent of artificial intelligence," as Harvard Business School professors Marco Iansiti and Karim Lakhani explain in their book Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World.
While normal education suffered a standstill in 2020, there were a lot of online courses and programs that were initiated by some of the most prestigious institutions as well as big tech giants so that the process of learning and skill development doesn't suffer. As the trend has been for a few years now, some of the most interesting initiatives were seen in the field of data science. In this article, we have listed some of the prominent data science education programs and initiatives in 2020. Microsoft, in collaboration with Netflix, has launched three new learning modules on beginners concepts in data science, along with machine learning and artificial intelligence. The design of these courses is inspired by the Netflix original film -- 'Over The Moon,' where a young girl Fei Fei, who builds a rocket to the moon, embarks on a mission to prove the existence of Moon Goddess.
We live in a world that is getting more divided each day. In some parts of the world, the differences and inequalities between races, ethnicities, and sometimes sexes are aggravating. The data we use for modeling is, in the major part, a reflection of the world it derives from. And the world can be biased, so data and therefore the model will likely reflect that. We propose a way in which ML engineers can easily check if their model is biased.
I'm interested to hear about other ML engineers in this sub: how you got to where you are; what you would've done differently; what a day in your life looks like. For me, I have a background in Econometrics and have been programming pretty heavily over the last 3 years. I only took one CS class in school – I wish I would've taken more. As you might imagine, I spend a lot of time each day programming. I don't do a whole lot of data engineering – I'm more on the model-deployment side of things.
MLOps refers to machine learning operations. It is a practice that aims to make machine learning in production efficient and seamless. While the term MLOps is relatively nascent, it draws comparisons to DevOps in that it's not a single piece of technology but rather a shared understanding of how to do things the right way. The shared principles MLOps introduces encourage data scientists to think of machine learning not as individual scientific experiments but as a continuous process to develop, launch, and maintain machine learning capabilities for real-world use. Machine learning should be collaborative, reproducible, continuous, and tested. The practical implementation of MLOps involves both adopting certain best practices and setting up an infrastructure that supports these best practices.
The growth of myriad cyber-threats continues to accelerate, yet the stream of new and effective cyber-defense technologies has grown much more slowly. The gap between threat and defense has widened, as our adversaries deploy increasingly sophisticated attack technology and engage in cyber-crime with unprecedented power, resources, and global reach. We are in an escalating asymmetric cyber environment that calls for immediate action. The extension of cyber-attacks into the socio-techno realm and the use of cyber as an information influence and disinformation vector will continue to undermine our confidence in systems. The unknown is a growing threat in our cyber information systems.