"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
When prompted to generate "a mural of a blue pumpkin on the side of a building," OpenAI's new deep ... [ ] learning model DALL-E produces this series of original images. OpenAI has done it again. Earlier this month, OpenAI--the research organization behind last summer's much-hyped language model GPT-3--released a new AI model named DALL-E. While it has generated less buzz than GPT-3 did, DALL-E has even more profound implications for the future of AI. In a nutshell, DALL-E takes text captions as input and produces original images as output. For instance, when fed phrases as diverse as "a pentagonal green clock," "a sphere made of fire" or "a mural of a blue pumpkin on the side of a building," DALL-E is able to generate shockingly accurate visual renderings.
The B2B model focuses on selling services and products to other companies. More than one person is often involved in buying decisions, which pushes companies to create a meaningful product and customer service that can drive the business. There are potential benefits of using AI in the B2B space to make better decisions and produce faster results. That is why AI is gradually taking on various job functions. AI has impacted every industry, be it business intelligence, productivity, customer management, recruiting, sales and marketing, healthcare, etc.
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."
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.
Anewly designed artificial intelligence tool based on the structure of the brain has identified a molecule capable of wiping out a number of antibiotic-resistant strains of bacteria, according to a study published on February 20 in Cell. The molecule, halicin, which had previously been investigated as a potential treatment for diabetes, demonstrated activity against Mycobacterium tuberculosis, the causative agent of tuberculosis, and several other hard-to-treat microbes. The discovery comes at a time when novel antibiotics are becoming increasingly difficult to find, reports STAT, and when drug-resistant bacteria are a growing global threat. The Interagency Coordination Group (IACG) on Antimicrobial Resistance convened by United Nations a few years ago released a report in 2019 estimating that drug-resistant diseases could result in 10 million deaths per year by 2050. Despite the urgency in the search for new antibiotics, a lack of financial incentives has caused pharmaceutical companies to scale back their research, according to STAT. "I do think this platform will very directly reduce the cost involved in the discovery phase of antibiotic development," coauthor James Collins of MIT tells STAT.
The field of deep learning has gained popularity with the rise of available processing power, storage space, and big data. Instead of using traditional machine learning models, AI engineers have been gradually switching to deep learning models. Where there is abundant data, deep learning models almost always outperform traditional machine learning models. Therefore, as we collect more data at every passing year, it makes sense to use deep learning models. Furthermore, the field of deep learning is also growing fast.
A study by Vuno, a Korean artificial intelligence (AI) developer, showed that a deep learning algorithm could predict Alzheimer's disease (AD) within one minute. Jointly with Asan Medical Center, Vuno verified an AI algorithm using MRI scans of 2,727 patients registered at domestic medical institutions. Vuno found that the algorithm predicted AD and mild cognitive impairment (MCI) accurately. Vuno's deep learning-based algorithm used an area under the curve (AUC) to predict dementia. The closer the AUC value is, the higher the algorithm's performance is.
In the last decades, artificial intelligence has shown to be very good at achieving exceptional goals in several fields. Chess is one of them: in 1996, for the first time, the computer Deep Blue beat a human player, chess champion Garry Kasparov. A new piece of research shows now that the brain strategy for storing memories may lead to imperfect memories, but in turn, allows it to store more memories, and with less hassle than AI. The new study, carried out by SISSA scientists in collaboration with Kavli Institute for Systems Neuroscience & Centre for Neural Computation, Trondheim, Norway, has just been published in Physical Review Letters. Neural networks, real or artificial, learn by tweaking the connections between neurons.
Artificial intelligence is slowly making its way into every industry, such as transportation and healthcare. Those with the ability to sift through volumes of data to identify insights are best equipped to succeed in an AI-driven job market. If you're interested in a career in AI, then you need to add Python to your skillset. Python is an extremely popular programming language, and it happens to be one of the easiest to learn, especially with The Ultimate Python & Artificial Intelligence Certification Bundle. These expert-taught online courses are normally $199 apiece, but ZDNet readers can grab the set for 97% off, dropping the price to $39.99.
Neural network models suffer from the phenomenon of catastrophic forgetting: a model can drastically lose its generalization ability on a task after being trained on a new task. This usually means a new task will likely override the weights that have been learned in the past (see Figure 1), and thus degrade the model performance for the past tasks. Without fixing this problem, a single neural network will not be able to adapt itself to a continuous learning scenario, because it forgets the existing information/knowledge when it learns new things. For realistic applications of deep learning, where continual learning can be crucial, catastrophic forgetting would need to be avoided. However, there is only limited study about catastrophic forgetting and its underlying causes.