"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Healthcare is a human right, however, nobody said all coverage is created equal. Artificial intelligence and machine learning systems are already making impressive inroads into the myriad fields of medicine -- from IBM's Watson: Hospital Edition and Amazon's AI-generated medical records to machine-formulated medications and AI-enabled diagnoses. But in the excerpt below from Frank Pasquale's New Laws of Robotics we can see how the promise of faster, cheaper, and more efficient medical diagnoses generated by AI/ML systems can also serve as a double-edged sword, potentially cutting off access to cutting-edge, high quality care provided by human doctors. Excerpted from New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale, published by The Belknap Press of Harvard University Press. We might once have categorized a melanoma simply as a type of skin cancer.
The field of artificial intelligence is moving at a staggering clip, with breakthroughs emerging in labs across MIT. Through the Undergraduate Research Opportunities Program (UROP), undergraduates get to join in. In two years, the MIT Quest for Intelligence has placed 329 students in research projects aimed at pushing the frontiers of computing and artificial intelligence, and using these tools to revolutionize how we study the brain, diagnose and treat disease, and search for new materials with mind-boggling properties. Rafael Gomez-Bombarelli, an assistant professor in the MIT Department of Materials Science and Engineering, has enlisted several Quest-funded undergraduates in his mission to discover new molecules and materials with the help of AI. "They bring a blue-sky open mind and a lot of energy," he says. "Through the Quest, we had the chance to connect with students from other majors who probably wouldn't have thought to reach out."
In this post we continue our summaries of the NeurIPS invited talks from the 2020 meeting. Here, we cover the talks by Chris Bishop (Microsoft Research) and Saiph Savage (Carnegie Mellon University). Chris began his talk by suggesting that now is a particularly exciting time to be involved in AI. What he termed "the real AI revolution" has nothing to do with artificial general intelligence (AGI), but is driven by the way we create software, and hence new technology. Machine learning is becoming ubiquitous and can be used to solve many problems that cannot, yet, be solved using other methods.
On 3 June 2020, the VUB AI Experience Centre published a webinar on the topic of the role of AI in the COVID-19 crisis, focused on macro dynamics predictions in the COVID-19 crisis, explained by micro intentions. This webinar focused on AI reinforcement learning techniques and predictive modelling, decision making in defining prevention, and exit strategies. It was led by Prof. dr. Ann Nowé from the Artificial Intelligence Lab together with Prof. dr Kurt Barbé, member of the Digital Mathematics research group and the cross-faculty Artificial Intelligence Lab, and Prof. dr Tom Lenaerts who is a member of the VUB Artificial Intelligence Lab and the Machine Learning Group of the ULB. The AI Experience Centre is a joint project of 4 VUB research groups: the Artificial Intelligence Lab, Brubotics, SMIT and ETRO, and is located on the VUB campus Etterbeek.
A panel of parents give there take on the president's move to reopen schools on'Fox & amp; Friends.' Maryland Gov. Larry Hogan is going all in on a push to reopen schools in the state for hybrid learning by the beginning of March. Hogan said during a news conference at St. John's College in Annapolis on Thursday that there is a growing consensus in the state and in the country that there is "no public health reason for county school boards to keep students out of schools" due to COVID-19. He argued that continuing down a path of virtual learning could lead to significant setbacks for students, especially among students of color and those from low-income families. "I understand that in earlier stages of the pandemic, that this was a very difficult decision for county school boards to make," Hogan added.
Vulnerable elephant populations are now being tracked from space using Earth-observation satellites and a type of artificial intelligence (AI) called machine learning. As part of an international project, researchers are using satellite images processed with computer algorithms, which are trained with more than 1,000 images of elephants to help spot the creatures. With machine learning, the algorithms can count elephants even on'complex geographical landscapes', such as those dotted with trees and shrubs. Researchers say this method is a promising new tool for surveying endangered wildlife and can detect animals with the same accuracy as humans. Elephants in woodland as seen from space.
Last year, we identified blockchain, cloud, open-source, artificial intelligence, and knowledge graphs as the five key technological drivers for the 2020s. Although we did not anticipate the kind of year that 2020 would turn out to be, it looks like our predictions may not have been entirely off track. Let's pick up from where we left off, retracing developments in key technologies for the 2020s: Artificial intelligence and knowledge graphs, plus an honorable mention to COVID-19-related technological developments. This TechRepublic Premium ebook compiles the latest on cancelled conferences, cybersecurity attacks, remote work tips, and the impact this pandemic is having on the tech industry. In our opener for the 2020s, we laid the groundwork to evaluate the array of technologies under the umbrella term "artificial intelligence."
This observation--that to understand Proust's text requires knowledge of various kinds--is not a new one. We came across it before, in the context of the Cyc project. Remember that Cyc was supposed to be given knowledge corresponding to the whole of consensus reality, and the Cyc hypothesis was that this would yield human-level general intelligence. Researchers in knowledge-based AI would be keen for me to point out to you that, decades ago, they anticipated exactly this issue. But it is not obvious that just continuing to refine deep learning techniques will address this problem.
Last week, the U.S. Food and Drug Administration presented the organization's first Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. This plan portrays a multi-pronged way to deal with the Agency's oversight of AI/ML-based medical software. The Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan is a response to stakeholder input on the FDA's 2019 regulatory structure for AI and ML-based medical items. FDA additionally will hold a public workshop on algorithm transparency and draw in its stakeholders and partners on other key activities, for example, assessing predisposition in algorithms. While the Action Plan proposes a guide for propelling a regulatory framework, an operational structure gives off an impression of being further down the road.
I am a recent graduate of the Galvanize Data Science Immersive Bootcamp. In this Data Science Bootcamp we spent 3 months learning Statistics, Linear Algebra, Calculus, Machine Learning, SQL, and Python Programming. The San Francisco based program I attended was transferred from in-person to remote due to the COVID-19 pandemic. To say this experience was challenging would be an understatement. My official day at the Bootcamp started at 8:30 AM and ended at 8:30 PM Monday through Friday.