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Finance

Voice-powered AI Financial and Pension Helper to Aid Decisions

A voice-powered AI financial and pension helper has been launched to aid people in making finance decisions. Former wealth manager and financial advisor Elemi Atigolo developed Finley AI – which he claims is the first of its kind in the world. Finley AI can be instantly accessed via any Google Assistant-enabled device by speaking the words “Google talk to financial helper”. The voice-interactive programme cannot give personalised advice, but Elemi claims it can offer answers to general questions that users might ask a financial advisor, on topics such as family planning, working life, retirement and death. Elemi says that while most of the numerous financial apps available today focus on short-term goals, such as saving or reducing debt, they don’t help users to plan for the long term or encourage them to seek expert advice. Users can ask Finley AI to match them to a financial advisor via FindPensionAdvice.com, which was also developed by Elemi. Finley AI will soon also be available on Alexa by Amazon and Facebook. Read More

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Human Resource

AI To Monitor Staff Activity

Dozens of UK business owners are using AI to scrutinise staff behavior minute-to-minute by harvesting data on who emails whom and when, who accesses and edits files and who meets whom and when. The actions of 130,000 people in the UK and abroad are being monitored in real time by the Isaak system, which ranks staff members’ attributes. Designed by Status Today, a London company, it is the latest example of a trend for using algorithms to manage people, which trade unions fear creates distrust, but others predict could reduce the effects of bias. The system shows bosses how collaborative workers are and whether they are “influencers” or “change-makers”. The computer can compare activity data with qualitative assessments of workers from personnel files or sales performance figures to give managers a detailed picture of how behaviour affects output. The Isaak system has already gathered data on more than 1 bn actions, which it uses to pinpoint “central individuals within a network” to better allocate workload and responsibilities, “ultimately improving the overall workplace environment and reducing stress and overworking”. Read More

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Fashion

Shopping Made Super Easy! AI-based App Helps Shop Better by Creating 3D Avatars

Lyflike, an app developed by BigThinx, uses AI to create realistic, walkabout 3D avatars of people, which can help them instantly try on any clothing, from any picture. Once an individual registers on the app, they need to create an avatar by taking a selfie to generate the face and body shot to grab measurements. Once the avatar is generated, the person can control their movements and dress up by recreating outfits using images and a virtual closet. The app can also show where to buy the product or find something similar online. The app identifies the type of outfit in an image, the texture of each item, and recreates it to be ‘tried on’ by the avatar. It also displays sizes based on measurements and lets the person play around with different fabrics to experiment. The app also helps to design unique outfits by picking from extensive libraries with different garments, cuts, patterns, graphics, and fabrics. Read More

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Security

AI Chatbot Helps People Find Information on Scams and Frauds

USA.gov, the official online portal of the US Federal government, has launched an AI powered chatbot, Sam to automate the process of helping people find information on scams and frauds. The idea behind Sam came from the need to make the process of providing USA.gov's users with access to all the information the platform has stored in its database using an automated solution. Before starting to work on the chatbot, the USA.gov team interviewed 32 users, who shared their experiences with scams. After collecting all the answers of the interviewees, the responses were added to a whiteboard to get an overview of the issues that got people to browse USA.gov's website. Using these results as a foundation, USA.gov created a navigation flowchart to be used by Sam to direct users to the correct answer for their questions, with empathetic and friendly responses designed to make him seem more receptive and approachable. Sam's developers also plan to expand Sam's current capabilities to make him bilingual and to inquire users about how satisfied they are by its services. Read More

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Healthcare

AI Predicts Hospital Readmission Rates from Clinical Notes

Researchers at New York University and Princeton have developed a framework that evaluates clinical notes and autonomously assigns a risk score indicating whether patients will be readmitted within 30 days. To overcome abbreviations and jargons on clinical notes, the researchers used Google’s bidirectional encoder representations from transformers, or BERT — that captures interactions between distant words in sentences by incorporating global, long-range information. Each clinical note is represented as a collection of tokens, or sub word units extracted from text in a pre-processing step. From multiple sequences of these, ClinicalBERT identifies which tokens are associated with which sequence. To train ClinicalBERT, the team sourced a corpus of clinical notes. Then, drawing on the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-III), the researchers fine-tuned the system for clinical forecasting tasks. In an experiment involving 48 or 72 hours of concatenated notes from 34,560 patients in the MIMIC-III corpus, the team claims that ClinicalBERT showed improved 30-day readmission prediction over models that focus solely on discharge summaries, yielding a 15.0% relative increase on recall. Read More

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Miscellaneous

AI To Spot Missing Citations

Around 25.0% of Wikipedia’s English-language articles lack a single citation. The challenge for Wikipedia is not merely adding more citations, though; it’s understanding where citations are needed in the first place. To solve this twofold problem, Wikimedia developed a twofold solution. Step one was to create a framework for understanding where citations need to go and create a data set. Step two was to train a machine learning model classifier to scan and flag those items across Wikipedia’s hundreds of thousands of articles. To get there, a roster of 36 English, Italian, and French Wikipedia editors were given text samples and were asked to put together a taxonomy of reasons like why a citation is needed and reasons on why they are not needed. With a set of guidelines in place, Wikimedia’s researchers created a data set upon which to train a recurrent neural network (RNN). Then, based on a sequence of words in a given sentence, the RNN was able to classify citation needs with 90.0% accuracy. Wikimedia’s researchers then created a second model that could add reasons to its citation classifications. Read More

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Miscellaneous

Google’s Best AI Flunked a High School Math Test

Faced with the same level of exam that a 16-year-old in the UK would take, Google’s DeepMind, its cutting-edge AI flunked. The algorithm was trained on the sorts of algebra, calculus, and other types of math questions that would appear on a 16-year-old’s math exam according to the UK national curriculum. The researchers tested several types of AI and found that algorithms struggle to translate a question as it appears on a test, full of words and symbols and functions, into the actual operations needed to solve it. It turns out, according to the research, that even a simple math problem involves a great deal of brainpower, as people learn to automatically make sense of mathematical operations, memorise the order in which to perform them and know how to turn word problems into equations. But AI is quite literally built to pore over data, scanning for patterns and analysing them. In that regard, the results of the test — on which the algorithm scored a 14 out of 40 — aren’t reassuring! Read More

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Entertainment

AI to Help Write Scripts

The idea of using computers to help write scripts and other tasks is gaining serious traction in Hollywood. Entertainment companies are using the technology to color-correct scenes, identify popular themes in book adaptations and craft successful marketing campaigns. Award-winning filmmaker, Kevin Macdonald, worked with a script written by a machine. Macdonald directed a 60-second Lexus sedan commercial using AI that relied on tech giant IBM’s platform, Watson. The computer produced a script featuring a sentient-like Lexus ES that hits the open road, whizzing by stunning vistas of shoreline and forests before saving itself from a dramatic crash. The AI was fed 15 years’ worth of award-winning car and luxury products ads as well as consumer insights data. This helped the machine identify what would resonate with consumers, which the AI interpreted to mean limited dialogue and a handful of visually appealing scenes, including a winding road that showed water on one side and trees on the other. Read More

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“Quantum Computing, a revolution in technology, has shown great promise towards solving complex computing problems currently outside the capabilities of current computers. Though in its infancy, we at Decimal Point Analytics strongly believe that it is going to grow exponentially in the near future. It holds the potential not only to boost the AI revolution but also transform the way data is synthesized. The articles on Quantum computing will give an insight into the recent developments in this space.”

 
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Quantum Computing

Quantum Computers Can Help to Avoid Global Financial Crisis

Super-fast computers could make financial firms much better at calculating risk – helping to avoid the calamitous events of the 2008 financial crash. Back during the financial crisis of 2007-2008, poor risk assessment was a key troublemaker. Calculating risk – and understanding how different kinds of risk are related to each other – is an extremely complex job. But banks and hedge funds think that quantum computing might help them reduce the risk lurking in their investment portfolios. Another big issue is the need to settle thousands of trades – matching up buyers and sellers with prices both sides agree on – at millisecond speed and high volume. The more efficient the settlement process, the better a bank can perform. Read More

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