Goto

Collaborating Authors

Industry


Council Post: Three Emerging Educational Opportunities In The Metaverse

#artificialintelligence

As the metaverse industry is expected to be an $800 billion market by 2024, we continue to learn new ways this immersive, virtual environment might better enable us to connect with each other from anywhere in the world. This comes at a time when many are already participating in and benefitting from virtual activities that otherwise would not be possible due to constraints of distance, time or cost. In enabling new opportunities for virtual rather than in-person instruction, the metaverse has the power to transform access to education and the way we learn. The types of education that the metaverse can accommodate are varied, from school-based interactive learning and workplace training to professional accreditation. In so many ways, the metaverse is offering new chances for people to learn what they want by mitigating obstacles of accessibility.


A systematic review of federated learning applications for biomedical data

#artificialintelligence

Author summary Interest in machine learning as applied to challenges in medicine has seen an exponential rise over the past decade. A key issue in developing machine learning models is the availability of sufficient high-quality data. Another related issue is a requirement to validate a locally trained model on data from external sources. However, sharing sensitive biomedical and clinical data across different hospitals and research teams can be challenging due to concerns with data privacy and data stewardship. These issues have led to innovative new approaches for collaboratively training machine learning models without sharing raw data. One such method, termed ‘federated learning,’ enables investigators from different institutions to combine efforts by training a model locally on their own data, and sharing the parameters of the model with others to generate a central model. Here, we systematically review reports of successful deployments of federated learning applied to research problems involving biomedical data. We found that federated learning links research teams around the world and has been applied to modelling in such as oncology and radiology. Based on the trends we observed in the studies reviewed in our paper, we observe there are opportunities to expand and improve this innovative approach so global teams can continue to produce and validate high quality machine learning models.


Sony's new headphones boast Endel's generative soundscapes – TechCrunch

#artificialintelligence

The other day, Brian reported on Sony's new LinkBuds headphones, including its partnership with "what if Brian Eno was a piece of computer software" app Endel. The company uses really fascinating AI technology to generate soundscapes and music tracks to help your brain do its best work -- to help you focus deeper, sleep more easily or to relax you. I spoke with one of Endel's founders to learn more about the tech and its deal with Sony. "Endel is first and foremost a technology that was built to help you focus, relax and sleep. And the way this technology works, it procedurally generates a soundscape in real time on the spot, on the device. It is personalized to you based on a number of inputs that we collect about you; things like the time of day, your heart rate, the weather, your movement and your circadian rhythms, like how much sleep you got last night," explains Oleg Stavitsky, CEO and co-founder at Endel.


7 Scikit-learn Utilities to Generate Artificial (Synthetic) Data

#artificialintelligence

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.


Artificial intelligence makes a splash in small-molecule drug discovery

#artificialintelligence

In the past five years, interest in applying artificial intelligence (AI) approaches in drug research and development (R&D) has surged. Driven by the expectation of accelerated timelines, reduced costs and the potential to reveal hidden insights from vast datasets, more than 150 companies with a focus on AI have raised funding in this period, based on an analysis of the field by Back Bay Life Science Advisors (Figure 1a). And the number of financings and average amount raised soared in 2021. At the forefront of this field are companies harnessing AI approaches such as machine learning (ML) in small-molecule drug discovery, which account for the majority of financings backed by venture capital (VC) in recent years (Figure 1b), as well as some initial public offerings (IPOs) for pioneers in the area (Table 1). Such companies have also attracted large pharma companies to establish multiple high-value partnerships (Table 2), and the first AI-based small-molecule drug candidates are now in clinical trials (Nat.


Global Big Data Conference

#artificialintelligence

Researchers at Duke University have demonstrated that incorporating known physics into machine learning algorithms can help the inscrutable black boxes attain new levels of transparency and insight into material properties. In one of the first projects of its kind, researchers constructed a modern machine learning algorithm to determine the properties of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields. Because it first had to consider the metamaterial's known physical constraints, the program was essentially forced to show its work. Not only did the approach allow the algorithm to accurately predict the metamaterial's properties, it did so more efficiently than previous methods while providing new insights. The results appear online the week of May 9 in the journal Advanced Optical Materials.


Can artificial intelligence overcome the challenges of the health care system?

#artificialintelligence

Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. The Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), a research center within the MIT Schwarzman College of Computing, recently hosted the MITxMGB AI Cures Conference in an effort to accelerate the adoption of clinical AI tools by creating new opportunities for collaboration between researchers and physicians focused on improving care for diverse patient populations. Once virtual, the AI Cures Conference returned to in-person attendance at MIT's Samberg Conference Center on the morning of April 25, welcoming over 300 attendees primarily made up of researchers and physicians from MIT and Mass General Brigham (MGB). MIT President L. Rafael Reif began the event by welcoming attendees and speaking to the "transformative capacity of artificial intelligence and its ability to detect, in a dark river of swirling data, the brilliant patterns of meaning that we could never see otherwise."


Operationalizing Machine Learning from PoC to Production - KDnuggets

#artificialintelligence

Many companies use machine learning to help create a differentiator and grow their business. However, it's not easy to make machine learning work as it requires a balance between research and engineering. One can come up with a good innovative solution based on current research, but it might not go live due to engineering inefficiencies, cost and complexity. Most companies haven't seen much ROI from machine learning since the benefit is realized only when the models are in production. Let's dive into the challenges and best practices that one can follow to make machine learning work.


AI may be searching you for guns the next time you go out in public

#artificialintelligence

When Peter George saw news of the racially motivated mass-shooting at the Tops supermarket in Buffalo last weekend, he had a thought he's often had after such tragedies. "Could our system have stopped it?" he said. But I think we could democratize security so that someone planning on hurting people can't easily go into an unsuspecting place." George is chief executive of Evolv Technology, an AI-based system meant to flag weapons, "democratizing security" so that weapons can be kept out of public places without elaborate checkpoints. As U.S. gun violence like the kind seen in Buffalo increases -- firearms sales reached record heights in 2020 and 2021 while the Gun Violence Archive reports 198 mass shootings since January -- Evolv has become increasingly popular, used at schools, stadiums, stores and other gathering spots. To its supporters, the system is a more effective and less obtrusive alternative to the age-old metal detector, making events both safer and more pleasant to attend. To its critics, however, Evolv's effectiveness has hardly been proved. And it opens up a Pandora's box of ethical issues in which convenience is paid for with RoboCop surveillance. "The idea of a kinder, gentler metal detector is a nice solution in theory to these terrible shootings," said Jay Stanley, senior policy analyst for the American Civil Liberties Union's project on speech, privacy, and technology. "But do we really want to create more ways for security to invade our privacy?


Top 10 AI graduate degree programs

#artificialintelligence

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI. U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2021 and early 2022. Here are the top 10 programs that made the list as having the best AI graduate programs in the US.