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Overjet raises $7.85M for its dental-focused AI tech – TechCrunch

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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.


Government presses ahead with Cummings' data science revolution

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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.


Predicting the Stock price Using TensorFlow

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A very simple graph that adds two numbers together. In the figure above, two numbers are supposed to be added. Those numbers are stored in two variables, a and b. The two values are flowing through the graph and arrive at the square node, where they are being added. The result of the addition is stored into another variable, c.


AI's Man Behind the Curtain - ReadWrite

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As the world grows increasingly connected, growing concern regarding the influence of artificial intelligence (AI) has been bubbling to the surface, affecting perceptions by industries big and small along with the general populace. Spurred on by sensationalized media predictions of AI taking over human decision-making and silver-screen tales of robot revolutions, there is a fear of allowing AI or its cousin, the Internet of Things (IoT), into our lives. Here is AI's man behind the curtain. One of the biggest sticking points is the popular – yet mistaken – notion that AI will cost people their jobs. In truth, the situation is just the opposite.


IIT-Ropar and TSW Launch a PG Programme in Artificial Intelligence

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IIT-Ropar, one of the eight new IITs established by the Ministry of Human Resource Development (MHRD), Government of India, and TSW, the executive education division of Times Professional Learning (a part of The Times of India Group), have launched a Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The programme will be coordinated by The Indo-Taiwan Joint Research Centre (ITJRC) on Artificial Intelligence (AI) and Machine Learning (ML), at IIT-Ropar. Supported by the Ministry of Science and Technology, Taiwan, ITJRC is a bilateral centre for collaborative research in disruptive technologies like AI and ML. The programme, with its focus on Artificial Intelligence and Deep Learning, has an eligibility criterion of a minimum of 2 years of work experience in the IT industry. Though an engineering degree is a desirable prerequisite for this programme, one does not need a coding or mathematics background to be eligible.


COMET: An Application of Model-Based Reasoning to Accounting Systems

AI Magazine

An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. Accounting sys-ems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. To cope with this complexity and variability, the COMET system applies a model-based reasoning approach to the analysis of accounting systems and their controls.


Does Machine Learning Really Work?

AI Magazine

Does machine learning really work? Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value. Machine-learning algorithms have now learned to detect credit card fraud by mining data on past transactions, learned to steer vehicles driving autonomously on public highways at 70 miles an hour, and learned the reading interests of many individuals to assemble personally customized electronic newsAbstracts. A new computational theory of learning is beginning to shed light on fundamental issues, such as the trade-off among the number of training examples available, the number of hypotheses considered, and the likely accuracy of the learned hypothesis. Newer research is beginning to explore issues such as long-term learning of new representations, the integration of Bayesian inference and induction, and life-long cumulative learning.


Making Better Recommendations with Online Profiling Agents

AI Magazine

In recent years, we have witnessed the success of autonomous agents applying machine-learning techniques across a wide range of applications. However, agents applying the same machine-learning techniques in online applications have not been so successful. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged. To a large extent, the reinforcement learning methods developed to aid agents in learning have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them somewhat ineffective in online applications.


Why is Artificial Intelligence So Useless for Business? - Matthew Eric Bassett, Ph.D.

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Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers. Yet I know that many businesses still need people to, e.g., read PDF documents about an office building and write down the sizes of the leasable units contained therein.


Custom DU: A Web-Based Business User-Driven Automated Underwriting System

AI Magazine

Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules.