If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Two shoebox-sized supercomputer satellites, built in Scotland to monitor shipping movements from low-Earth orbit, are due for launch this afternoon. Each nanosatellite has an onboard supercomputer with machine learning algorithms that can provide'hyper-accurate predictions' of the locations of boats. The the so-called'Spire' satellites will calculate their arrival times at ports to help businesses and authorities manage busy docks, the UK Space Agency said. They will join a fleet of more than 100 objects in low Earth orbit that work together to track the whereabouts of ships and predict global ocean traffic. Two of the satellites will launch at lunchtime today and another couple will launch on an Indian PSLV rocket on November 1.
A way of monitoring household appliances by using machine learning to analyse vibrations on a wall or ceiling has been developed by researchers in the US. Their system could be used to create centralized smart home systems without the need for individual sensors in each object. What is more, the technology could help track energy use, identify electrical faults and even remind people to empty the dishwasher. "Recognizing home activities can help computers better understand human behaviours and needs, with the hope of developing a better human-machine interface," says team member and information scientist Cheng Zhang of Cornell University. The system, dubbed VibroSense, comprises two core parts: a laser Doppler vibrometer and a deep learning model, which is a type of machine learning system.
We are very excited to release the free tier of dunnhumby Model Lab this as part of our partnership with Microsoft. We make it easy to connect your data, clean your data, and run your machine learning pipeline within minutes. You can then take that output and copy right into a notebook for further refinement if needed. You can create new projects, reference datasets, and create multiple experiments in just a few clicks! You can also follow the progress of your machine learning experiments as they update in real-time.
BEIJING--(BUSINESS WIRE)--Artificial Intelligence Defense Platform, a technology start-up creating AI technology for a safer, more comfortable future, and its Founder Andy Khawaja have created a new department within AIDP that is dedicated to pandemic research to see how AI technology can be most useful in future pandemics. In lieu of the Coronavirus Pandemic, or COVID-19, Artificial Intelligence Defense Platform has already found many new ways AI technology can be used for identifying and forecasting pandemic outbreaks. AI technology can track the outbreaks and predict outcomes based on actions. Andy Khawaja and AIDP have found better methods for tracking carriers based upon tracing data via smartphones, also known as contact tracing. Although there is a natural resistance to artificial intelligence technology in the healthcare industry, AIDP's studies show how useful the technology can be in the future to minimize contact when necessary.
Hostile and hateful remarks are thick on the ground on social networks in spite of persistent efforts by Facebook, Twitter, Reddit and YouTube to tone them down. Now researchers at the OpenWeb platform have turned to artificial intelligence to moderate Internet users' comments before they are even posted. The method appears to be effective because one third of users modified the text of their comments when they received a nudge from the new system, which warned that what they had written might be perceived as offensive. The study conducted by OpenWeb and Perspective API analysed 400,000 comments that some 50,000 users were preparing to post on sites like AOL, Salon, Newsweek, RT and Sky Sports. Some of these users received a feedback message or nudge from a machine learning algorithm to the effect that the text they were preparing to post might be insulting, or against the rules for the forum they were using.
Infer Genetic Disease From Your Face - DeepGestalt can accurately identify some rare genetic disorders using a photograph of a patient's face. This could lead to payers and employers potentially analyzing facial images and discriminating against individuals who have pre-existing conditions or developing medical complications.
Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functions. However, these can instead be more faithfully emulated by higher-order circuit elements that naturally express neuromorphic nonlinear dynamics1–4. Generating neuromorphic action potentials in a circuit element theoretically requires a minimum of third-order complexity (for example, three dynamical electrophysical processes)5, but there have been few examples of second-order neuromorphic elements, and no previous demonstration of any isolated third-order element6–8. Using both experiments and modelling, here we show how multiple electrophysical processes—including Mott transition dynamics—form a nanoscale third-order circuit element. We demonstrate simple transistorless networks of third-order elements that perform Boolean operations and find analogue solutions to a computationally hard graph-partitioning problem. This work paves a way towards very compact and densely functional neuromorphic computing primitives, and energy-efficient validation of neuroscientific models. Electrophysical processes are used to create third-order nanoscale circuit elements, and these are used to realize a transistorless network that can perform Boolean operations and find solutions to a computationally hard graph-partitioning problem.
Technical skills and data literacy are obviously important in this age of AI, big data, and automation. But that doesn't mean we should ignore the human side of work – skills in areas that robots can't do so well. I believe these softer skills will become even more critical for success as the nature of work evolves, and as machines take on more of the easily automated aspects of work. In other words, the work of humans is going to become altogether more, well, human. With this in mind, what skills should employees be looking to cultivate going forward?
My name is Palash Shah, and I'm the author of Libra: a machine learning library that lets you build and train models in one line of code. My journey in the open source community started as a normal college student -- I worked on my library after classes in my dorm room. Quite quickly though, it began to grow into something much bigger than that, going from 0 to close to 2,000 stars in under a month. All of a sudden it was being used at universities like Carnegie Mellon and MIT in several of their machine learning classes. As someone who previously had no professional presence and/or connections in the technical industry, my experience starting this project was unique compared to the rest of the players in this space.
LSTMs are one of the most important breakthroughs in machine learning; Giving machine learning algorithms the ability to recall past information, allows for the realization of temporal patterns. How better to understand a concept, than to create it from scratch? LSTM stands for Long short-term memory, denoting its ability to use past information to make predictions. The mechanism behind the LSTM is quite simple. Instead of a single feedforward process for the data to be propagated through, LSTMs have different sources of processed information, from different timesteps as inputs for the networks, therefore being able to access time-related patterns within the data.