The internet is rife with myths and articles making dubious claims about certain foods and their anti-cancer properties. We have all seen the articles of questionable scientific merit gracing social media suggesting that such-and-such foods can cure cancer, the majority of which are highly questionable. A new study offers a unique kind of insight into the potential true effectiveness of food in fighting cancer [1].
Investigating molecules in food with machine learning
There is no doubt that there are many foods that contain a myriad of active molecules, and perhaps some of these food myths may have a grain of truth to them. A team of researchers decided to do some real myth-busting and put a variety of bioactive molecules found in foods to the test to see if they might potentially help to combat cancer.
Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.
How might the application of artificial intelligence enhance the experience and reach of electronic gaming and gambling?
Over the next few years, the internet gaming business could be transformed completely as artificial intelligence (AI) enters the scene. At its core, AI is a type software or hardware that learns—and it could be programmed to learn mostly about us, its users and those insights could drive the developments of new, hyper-personalised gaming and internet betting experiences. The technology is being applied to learn our habits, our likes, and our relationship patterns. Just as Netflix uses an algorithm to suggest films you might watch, the concept of personalisation is extending to the idea of “Lifestyle AI” applications that could help choose your entertainment, gaming choices, wardrobe, your next meal, your job, and romantic partner. Take this one natural step further, and we enter the domain of mass tailoring of gaming and betting experiences.
While it all sounds a bit like science fiction, the capabilities of AI tools and the range of applications are growing exponentially. Indeed, by 2020 AI could be present in some form in everything we do, and by 2030, AI is likely to have infiltrated our lives in much the same way as smartphones, the internet, and global travel are now taken for granted. So how might AI change our recreational habits and day-to-day existence in a way that might affect e-gaming? Here are eight novel ways internet betting could be different in future as a result of AI.
Trend Betting – Individuals could bet on the word, phrase, issue, or concept that will be mentioned most across a range of sites on the web during a fixed period, and then AI web crawlers would determine the actual count. Machine learning would be used to profile these trends and patterns over time, predict the likelihood and frequency of occurrence of key terms, and then determine the odds accordingly. Users could volunteer their own terms alongside those which the gambling sites suggest. To determine the initial odds for new terms, machine learning would compare the new term to others it has already analysed, and search the internet to see how frequently it is mentioned. The algorithm would then set the initial odds and refine them over time in response to actual betting patterns and payouts.
The authors of a recent paper believe that in the future, artificial intelligence might be able to tell benign from malignant lesions without a biopsy.
Elon Musk’s Neuralink, the secretive company developing brain-machine interfaces, showed off some of the technology it has been developing to the public for the first time. The goal is to eventually begin implanting devices in paralyzed humans, allowing them to control phones or computers.
A neuromorphic computer that can simulate 8 million neurons is in the news. The term “neuromorphic” suggests a design that can mimic the human brain. And neuromorphic computing? It is described as using very large scale integration systems with electric analog circuits imitating neuro-biological architectures in our system.
This is where Intel steps in, and significantly so. The Loihi chip applies the principles found in biological brains to computer architectures. The payoff for users is that they can process information up to 1,000 times faster and 10,000 times more efficiently than CPUs for specialized applications, e.g., sparse coding, graph search and constraint-satisfaction problems.
Its news release on Monday read “Intel’s Pohoiki Beach, a 64-Chip Neuromorphic System, Delivers Breakthrough Results in Research Tests.” Pohoiki Beach is Intel’s latest neuromorphic system.
Metamaterials are artificial materials engineered to have properties not found in naturally occurring materials, and they are best known as materials for invisibility cloaks often featured in sci-fi novels or games. By precisely designing artificial atoms smaller than the wavelength of light, and by controlling the polarization and spin of light, researchers achieve new optical properties that are not found in nature. However, the current process requires much trial and error to find the right material. Such efforts are time-consuming and inefficient; artificial intelligence (AI) could provide a solution for this problem.
The research group of Prof. Junsuk Rho, Sunae So and Jungho Mun of Department of Mechanical Engineering and Department of Chemical Engineering at POSTECH have developed a design with a higher degree of freedom that allows researchers to choose materials and design photonic structures arbitrarily by using deep learning. Their findings are published in several journals including Applied Materials and Interfaces, Nanophotonics, Microsystems & Nanoengineering, Optics Express, and Scientific Reports.
AI can be trained with a vast amount of data, and it can learn designs of various metamaterials and the correlation between photonic structures and their optical properties. Using this training process, it can provide a design method that makes a photonic structure with desired optical properties. Once trained, it can provide a desired design promptly and efficiently. This has already been researched at various institutions in the U.S. such as MIT, Stanford University and Georgia Institute of Technology. However, the previous studies require inputs of materials and structural parameters beforehand, and adjusting photonic structures afterwards.
But a less-noticed win for DeepMind, the artificial-intelligence arm of Google’s parent Alphabet Inc., at a biennial biology conference could upend how drugmakers find and develop new medicines. It could also dial up pressure on the world’s largest pharmaceutical companies to prepare for a technological arms race. Already, a new breed of upstarts are jumping into the fray.
Alphabet’s DeepMind cracked a problem that long vexed biologists, heating up a technological arms race in health care.
