Big Data Analytics Solutions | Financial Services in India |Decimal Point Analysis
Image 18 March 2019
Image
 
Image

Finance

Funds Use Artificial Intelligence to Weigh Ethical Investing

Establishing a link between corporate responsibility and financial outperformance has long been a priority of the sustainable investment industry. The field is knotted in dilemmas: corporate facades of piety, gaming of the ESG label, wildly disparate scoring methods and data that is dishevelled at best, non-existent at worst. A group of quants are exploring links between ESG investment and outperformance. Quant funds are using AI tools to excavate patterns in data in their search for evidence of correlation. Natixis Investment Managers' subsidiary Ossiam has launched a smart-beta ETF using machine learning techniques with a ESG focus. Research conducted by Ossiam suggests traditional ESG filtering ignores most information contained in the multitude of ESG indicators. Machine Learning can be used to analyse these large data sets and identify companies which are likely to outperform and those particularly risky to invest in from their ESG profile. This algorithm, ranking companies based on their ESG compliance and financial potential, will improve its company selection with the time as it learns from the data. The ETF's ESG filter will start ruling out those companies that have been subject to disputes; are involved in controversial weapons business; have significant operations in the tobacco or coal industry or do not comply with the UN Global Compact principles. Read More

Image
Image

Meteorology

Machine Learning Helps Improve Sub-seasonal Climate Forecasting

Judah Cohen, Director of Seasonal forecasting at Atmospheric and Environmental Research and visiting scientist in MIT's Department of Civil and Environmental Engineering and Ernest Fraenkel, Professor of Biological engineering at MIT Stanford University have devised a weather predicting model that has overpowered the operational long-range forecasting model used by the US government. The current weather predicting models are only able to make forecasts about 7 to 10 days prior to the forecast. By using machine learning techniques, the new model is able to help energy companies and cities prepare for severe storms much farther in advance. The dynamic team of experts combined historical weather-pattern recognition and machine learning in order to produce real-time predictions of temperature and precipitation anomalies 2 to 6 weeks in advance for the western US. The combination of machine learning techniques and historical weather-pattern recognition is very powerful because it can help the government maximize water resources and prepare for natural disasters or extreme weather conditions. Read More

Image
Image

Oil & Gas Industry

AI To Drill Oil Wells

Oil and gas giant, Royal Dutch Shell has recently drilled its fourth well in the Permian using AI. The well was drilled remotely from an operations office in Houston using only algorithms. The company is working with C3 IoT and uses Microsoft Azure as its AI platform. Wells drilled with AI have at least a 5% better estimated ultimate recovery than those drilled without AI because those wells stay in the prime drilling zone without deviating. The push for digital technologies comes as oil majors look to become more aggressive in shale. Drilling with AI is only just the start of what the oil and gas industry can do with the technology. It allows to learn and rapidly create better solutions. Read More

Image
Image

Technology

Cyberbullying? AI to the rescue!

The internet has been one of the greatest inventions of mankind. But sadly, not everyone uses it in the way it was intended to. Cyberbullying has become rampant and trolling is practised by millions. To deal with this, Jigsaw, a team under Alphabet is now using AI to make the internet a safer place. The team has created the Perspective API, whose objective is to make internet conversations better. It uses machine learning models to score the perceived social impact of a comment and makes it available to the developers, moderators and admins. Further action can be taken based on that data. To make it more accessible to the masses, there’s a Google Chrome extension called Tune, powered by machine learning, that will filter out the potentially toxic and negative content. Tune is an experimental Chrome extension from Jigsaw that lets people control how much of the bad stuff they want to see in comments. Once added, the user can select the level and infringements as per their needs. Read More

Image
Image

Human Resources

Meet Tengai, the AI-powered Job Interview Robot

Tengai, the world's first robot designed to carry out unbiased job interviews is being tested by Swedish recruiters. Tengai is the brainchild of Furhat Robotics, an AI and social robotics company born out of a research project at Stockholm's KTH Royal Institute of Technology. Since October 2018, the start-up's been collaborating with one of Sweden's largest recruitment firms, TNG. The goal is to offer candidates job interviews that are free from any of the unconscious biases. Tengai doesn't engage in pre-interview chit-chat and poses all questions in an identical way, in the same tone, and typically, in the same order. This is thought to create a fairer and more objective interview. Recruiters or managers are then given text transcripts of each interview to help them decide which candidates should move to the next stage of the process, based on the answers alone. Following several months of trials, Tengai will start interviewing candidates for real later in May. Recruiters and developers are now working on a more sophisticated version of Tengai, which will help to decide whether a candidate can move forward to the next stage of recruitment. Read More

Image
Image

Entertainment

AI – The Future of Music?

AI is making inroads in the music industry. YouTube star Taryn Southern, who doesn't know to play a single instrument, released the album titled “I Am AI”, featuring eight tracks produced entirely with AI, an unprecedented feat. The pop artist stated that she began experimenting with AI two years ago, working with Amper, an AI music composition software. Founded by a group of New York based engineers and musicians, Amper is part of about a dozen start-ups using AI to break with the traditional way of making music. The idea of Amper is to enable everyone to express themselves through music, regardless of their background and skills. The company relies on tons of source material -- from dance hits to classical music -- to produce custom songs. The Amper app allows a user to pick a genre of music (rap, folk, rock) and a mood (happy, sad, driving) before spitting out a song. The user can then change the tempo, add instruments or switch them out until the result is satisfactory. Read More

Image
Image

Content Management

AI To Detect Bot Written Text

OpenAI had built a text generating algorithm called GPT-2 that they said was too dangerous, since it could be used to pollute the web with endless bot-written material. Therefore, a team of scientists from the MIT-IBM Watson AI Lab and Harvard University built an algorithm called GLTR that determines how likely any particular passage of text was written by using a tool like GPT-2. The GLTR uses the exact same models as GPT-2, to read the final output and predict whether it was written by a human or not. It is based on the idea that computer generated text sticks to the most likely words, whereas natural writing more frequently selects unpredictable words that make sense to the domain. Further, the scientists behind the project built a website that lets people test GLTR for themselves. The tool highlights the words in different colours based on how likely they are to have been written by an algorithm like GPT-2. Green means the sentence matches GPT-2, and shades of yellow, red, and especially purple indicate that a human probably wrote them. Read More

Image
Image

Healthcare

Machine Learning Model Classifies Lung Cancer Slides in Under A Minute

Researchers at the Dartmouth-Hitchcock Medical Center have developed a machine learning model that can classify different types of lung cancer in less than a minute. The model could be used to assist doctors in determining tumour patterns and subtypes, which is an important part of prognosis and so will aid in determining the appropriate treatment. The machine learning model can perform on par with three practicing pathologists. In this model, the researchers used unsupervised machine learning, which means the model automatically trawls through millions of training data to identify subtle correlations and so teach itself. Lung adenocarcinoma, the most common type of lung cancer, currently requires pathologists to visually examine the lobectomy slides on a case-by-case basis. It is both a time consuming and subjective task, making machine learning well suited to expedite the process. As a result, scientists are increasingly turning to machine learning to assist with medical diagnosis. Read More

Image
Image

“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.”

 
Image

Quantum Computing

Physicists Manage to Reverse Time Using A Quantum Computer

An international team of scientists led by researchers at the Moscow Institute of Physics and Technology demonstrated the possibility of time reversal in a development that contradicts the basic laws of Physics. The researchers have artificially created a state that evolves in a direction opposite to that of the thermodynamic arrow of time. In a study published in the journal Scientific Reports on March 13, the scientists experimentally showcased time reversal by sending a qubit from a more complex state to a simpler one using an algorithm on an IBM quantum computer. The researchers attempted to calculate the probability of observing an electron over a fraction of a second spontaneously localising into its recent past. Later, they made the same attempt to reverse time using qubits. They employed a programme to convert the qubits into a more complex pattern of ones and zeros, and then used another programme to bring them back to their original state. This successfully caused the qubits to evolve backwards, from chaos towards order. The team found that in 85% of the cases, the two-qubit quantum computer returned back to the initial state. However, more errors occurred when a third qubit was introduced, with the success rate falling to around 50%. Read More

Image
Image

Decimal Point Analytics Pvt Ltd does not make any recommendation, solicitation, or offer for any securities and is not responsible for suitability of any securities for any purpose, investment or otherwise. It is the sole responsibility of the client, as a professional organization, to exercise professional due diligence in ensuring suitability of investment and ensuring that when the client publishes a part or full report under its own brand, the legal requirements for distribution of such material are complied with in all the jurisdiction in which it is published. Decimal Point Analytics Pvt. Ltd. shall not be responsible for any loss suffered by the user. The returns indicated, including future projections, in any investment report prepared by Decimal Point Analytics Pvt Ltd are not guaranteed in any manner and may not be achieved.

India :

5A, B-Wing, Trade Star Building, J. B. Nagar, Andheri-Kurla Road, Andheri (East), Mumbai - 400 059, Maharashtra, India
+91 22 30015200
info@decimalpointanalytics.com

United Kingdom:

1st Floor, 99, Bishopsgate, London, EC2M 3XD, United Kingdom

+ 44 20 3286 1998
info@decimalpointanalytics.com

USA:

17 State Street, Suite 4000, New York, NY 10004 U.S.A

+1 (917) 341 3218
info@decimalpointanalytics.com

www.decimalpointanalytics.com

×

Decimal Point Analytics (DPA) will process the information in this form to share information as requested. By checking the above box you confirm your acceptance to receive the communication. You can unsubscribe any time by clicking the ‘Unsubscribe’ link in the footer of any email you receive from us, or by contacting us at

We use cookies to measure website performance, provide social media features and personalize content.
Close this dialog to confirm you are happy with that, or find out more in the privacy statement.