Overjet, a startup focused on using AI to help dentists and insurance companies understand dental scans, today announced that it has raised $7.85 million in what it describes as a seed round. According to Overjet's CEO Wardah Inam (an MIT PhD in electrical engineering and computer science), the company raised the funds from Crosslink Capital, which led its round, and E14 Fund, which "only invests in MIT startups," Inam said. The MIT-E14 connection is not surprising, given that Overjet has been supported by two different MIT groups. Continuing the Boston-area educational links, the startup was incubated by the Harvard Innovation Lab, which Inam told TechCrunch that it is "growing out of" in terms of space. Inam told TechCrunch that Overjet was interested in raising from Crosslink thanks to its prior investments into Weave, a startup whose software is often used in a dental context.
In what now seems a distant pre-pandemic period, excitement about the potential of artificial intelligence (AI) in healthcare was already escalating. From the academic and clinical fields to the healthcare business and entrepreneurial sectors, there was a remarkable proliferation of AI -- e.g., attention-based learning, neural networks, online-meets-offline, and the Internet of Things. The reason for all this activity is clear -- AI presents a game-changing opportunity for improving healthcare quality and safety, making care delivery more efficient, and reducing the overall cost of care. Well before COVID-19 began to challenge our healthcare system and give rise to a greater demand for AI, thought leaders were offering cautionary advice. Robert Pearl, MD, a well-known advocate for technologically advanced care delivery, recently wrote in Forbes that because technology developers tend to focus on what will sell, many heavily marketed AI applications have failed to elevate the health of the population, improve patient safety, or reduce healthcare costs.
According to an unofficial consensus, the birth of artificial intelligence as an independent research project can be dated to the summer of 1956, when John McCarthy at Dartmouth College, where he belonged to the Mathematical Department, was able to persuade the Rockefeller Foundation to finance an investigation " The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it". In addition to McCarthy (who was a professor at Stanford University until 2000 and is responsible for the coining of the term "artificial intelligence"), several other participants took part in the historical workshop at Dartmouth: Marvin Minsky (former professor at Stanford University), Claude Shannon (inventor of information theory); Herbert Simon (Nobel Prize winner in economics); Arthur Samuel (developer of the first chess computer program at world champion level); furthermore half a dozen experts from science and industry, who dreamed that it might be possible to produce a machine for coping with human tasks, which, according to the previous opinion, require intelligence. The Manifesto of Dartmouth (written at the dawn of the AI age) is both irritating and blurred. It is not clear whether the conference participants believed that one-day, machines would think or behave as if they could imagine. Both possible interpretations allow the word "simulate."
A British artificial intelligence firm involved in the Vote Leave campaign has been handed a £400,000 contract to tap data from places such as social media sites to help steer the Government's response to Covid-19. Official documents from the Government show Faculty Science was awarded the contract by the Ministry of Housing, Communities and Local Government (MHCLG) in April to provide data scientists who could set up "alternative data sources (e.g. They would, the contract said, apply data science and machine learning to the data, which could help identify trends, and then develop "interactive dashboards" to inform policymakers. It is understood the contract, awarded through the Government's G-Cloud framework, was designed to address an urgent need for the department to analyse real-time data and monitor the effect of Covid-19 on local communities. Faculty's AI technology can be used to process vast amounts of data and in the past was used for polling analysis by the Vote Leave campaign, run by Boris Johnson's adviser Dominic Cummings.
Back in August 2019, the BBC made some waves with the news that it was developing a voice assistant called Beeb, an English language "Alexa" of its own that could interact with and control its array of radio and TV services, and its on-demand catalogue, and able to understand the array of accents you find in across the BBC's national footprint to boot. Ten months on, it's releasing its first live version of the service in the form of a beta to a select group of early adopters: UK-based members of the Windows Insider Program, a beta-testing, bug-seeking, early-adopter group popular in the Windows community, with over 10 million users globally. The idea with the limited release beta -- according to Grace Boswood, COO of BBC Design and Engineering -- will be to get Insiders to try out various features and stress test Beeb in the early beta, while at the same time giving the BBC a trove of usage data that can help it continue to train Beeb further, ahead of a wider release. The BBC is not naming a date yet for the general release. When you are a member in the UK, you have to be using the latest release of Windows 10, and then you download Beeb BETA form the Windows App Store.)
Machine learning is a method of data analysis that automates the creation of analytical models. It is a discipline of Artificial Intelligence based on the concept that systems can learn from data, identify patterns and make decisions without or with minimal human intervention. As data is constantly being produced, machine learning solutions adapt autonomously, learning from new information as well as from previous processes. Most companies that handle big data are recognizing the value of machine learning (for example, industrial learning, which obtains information from sources as diverse as the Internet of Things, sensors, etc.). If you want to get the most out of your business data and automate processes like you have never imagined before, now is the time to apply a machine learning strategy in your organization.
We live in fascinating times, where Deep Learning [DL] is continuously applied in new areas of our life and very often, revolutionizes otherwise stagnated industries. At the same time, open-source frameworks such as Keras and PyTorch level the playing field and give everybody access to state-of-the-art tools and algorithms. Strong community and simple API of these libraries make it possible to have cutting edge models at your fingertips, even without in-depth knowledge of math that makes it all possible. However, the understanding of what is happening inside the Neural Network [NN] helps a lot with tasks like architecture selection, hyperparameters tuning, or performance optimization. Since I believe that nothing teaches you more than getting your hands dirty, I'll show you how to create a Convolutional Neural Network [CNN] capable of classifying MNIST images, with 90% accuracy, using only NumPy.
Is it possible some instances of artificial intelligence are not as intelligent as we thought? Call it artificial artificial intelligence. A team of computer graduate students reports that a closer examination of several dozen information retrieval algorithms hailed as milestones in artificial research were in fact nowhere near as revolutionary as claimed. In fact, AI used in those algorithms were often merely minor tweaks of previously established routines. According to graduate student researcher Davis Blalock at the Massachusetts Institute of Technology, after his team examined 81 approaches to developing neural networks commonly believed to be superior to earlier efforts, the team could not confirm that any improvement, in fact, was ever achieved.
Then this course is for you! This course is designed in a very simple and easily understandable content. You might have seen lots of buzz on deep learning and you want to figure out where to start and explore. This course is designed exactly for people like you! If basics are strong, we can do bigger things with ease.
The Defense Department is seeking to adapt artificial intelligence technology it uses to track down terrorists with drones or predict when aircraft need maintenance for a new purpose: screening and testing novel coronavirus treatments and vaccines. The Pentagon plans to boost existing programs with money Congress provided under the virus-relief CARES Act for the "development of artificial intelligence-based models to rapidly screen, prioritize, and test Food and Drug Administration approved therapeutics for new COVID-19 drug candidates." The AI funds would also be tapped for human test trials for vaccines and antibody based treatments, according to the spending plan the department submitted to congressional panels. Dick Durbin (Ill.), the Senate's No. 2 Democrat and ranking member on the Appropriations Defense Subcommittee, pressed for the plan's release. While the amount of money the Pentagon wants to use on these programs is small---close to $1 million--it shows some of the department's urgency to apply new technology to choke off the pandemic.