The team examined digital images of the Northern sky obtained using a k-D tree in 2018 to detect space anomalies through the nearest neighbour method. The research then utilised machine learning algorithms to automate the research.

The study is published in New Astronomy.

Astronomical discoveries have increased drastically in recent years due to large-scale astronomical surveys. The Zwicky Transient Facility, for example, employs a wide-field view camera to survey the Northern sky, generating1.4 TB of data each night of observation with its catalogue containing billions of objects.

However, processing such colossal quantities of data manually is extremely expensive and time-consuming. To overcome this, the SNAD team, consisting of researchers from Russia, France, and the US, collaborated to devise an automated process.

When analysing astronomical objects, scientists observe their light curves, which demonstrate the variation of an objects brightness as a function of time. Scientists first identify a flash of light in the sky and then follow its evolution to see if it becomes brighter, weaker, or goes out.

In their study, the researchers analysed a million real light curves from the ZTFs 2018 catalogue and seven simulated live curve models of the types of objects being studied. They followed a total of 40 parameters, including the amplitude of an objects brightness and timeframe.

Konstantin Malanchev, co-author of the paper and postdoc at the University of Illinois at Urbana-Champaign, commented: We described the properties of our simulations using a set of characteristics expected to be observed in real astronomical bodies. In the dataset of approximately a million objects, we were looking for super-powerful supernovae, Type Ia supernovae, Type II supernovae, and tidal disruption events. We refer to such classes of objects as space anomalies. They are either very rare, with little-known properties, or appear interesting enough to merit further study.

Subsequently, the team compared light curve data from real objects to simulations using the k-D tree algorithm which is a geometric data structure for dividing space into smaller parts by cutting it with hyperplanes, planes, lines, or points. The algorithm was employed to narrow the search range when looking for real objects with similar properties to this in the seven simulations.

The researchers identified 15 nearest neighbours (real objects from the ZTF database) for each simulation 105 matches in total, which were then visually examined for space anomalies. The manual verification process confirmed 11 space anomalies seven were supernova candidates, and four were active galactic nuclei candidates where tidal disruption events could occur.

Maria Pruzhinskaya, a co-author of the paper and research fellow at the Sternberg Astronomical Institute, commented: This is a very good result. In addition to the already-discovered rare objects, we were able to detect several new ones previously missed by astronomers. This means that existing search algorithms can be improved to avoid missing such objects.

The study demonstrates that the method is highly effective and easy to apply. Moreover, the method is universal and can be used to discover any astronomical object, not just rare types of supernovae.

Matvey Kornilov, Associate Professor of the HSE University Faculty of Physics, concluded: Astronomical and astrophysical phenomena which have not yet been discovered are, in fact, anomalies. Their observed manifestations are expected to differ from the properties of known objects. In the future, we will try using our method to discover new classes of objects.

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11 new space anomalies discovered using Artificial Intelligence - Innovation News Network

It started with a single tweet in November 2019. David Heinemeier Hansson, a high-profile tech entrepreneur, lashed out at Apples newly launched credit card, calling it sexist for offering his wife a credit limit 20 times lower than his own.

The allegations spread like wildfire, with Hansson stressing that artificial intelligence now widely used to make lending decisions was to blame. It does not matter what the intent of individual Apple reps are, it matters what THE ALGORITHM theyve placed their complete faith in does. And what it does is discriminate. This is fucked up.

While Apple and its underwriters Goldman Sachs were ultimately cleared by US regulators of violating fair lending rules last year, it rekindled a wider debate around AI use across public and private industries.

Politicians in the European Union are now planning to introduce the first comprehensive global template for regulating AI, as institutions increasingly automate routine tasks in an attempt to boost efficiency and ultimately cut costs.

That legislation, known as the Artificial Intelligence Act, will have consequences beyond EU borders, and like the EUs General Data Protection Regulation, will apply to any institution, including UK banks, that serves EU customers. The impact of the act, once adopted, cannot be overstated, said Alexandru Circiumaru, European public policy lead at the Ada Lovelace Institute.

Depending on the EUs final list of high risk uses, there is an impetus to introduce strict rules around how AI is used to filter job, university or welfare applications, or in the case of lenders assess the creditworthiness of potential borrowers.

EU officials hope that with extra oversight and restrictions on the type of AI models that can be used, the rules will curb the kind of machine-based discrimination that could influence life-altering decisions such as whether you can afford a home or a student loan.

AI can be used to analyse your entire financial health including spending, saving, other debt, to arrive at a more holistic picture, Sarah Kocianski, an independent financial technology consultant said. If designed correctly, such systems can provide wider access to affordable credit.

But one of the biggest dangers is unintentional bias, in which algorithms end up denying loans or accounts to certain groups including women, migrants or people of colour.

Part of the problem is that most AI models can only learn from historical data they have been fed, meaning they will learn which kind of customer has previously been lent to and which customers have been marked as unreliable. There is a danger that they will be biased in terms of what a good borrower looks like, Kocianski said. Notably, gender and ethnicity are often found to play a part in the AIs decision-making processes based on the data it has been taught on: factors that are in no way relevant to a persons ability to repay a loan.

Furthermore, some models are designed to be blind to so-called protected characteristics, meaning they are not meant to consider the influence of gender, race, ethnicity or disability. But those AI models can still discriminate as a result of analysing other data points such as postcodes, which may correlate with historically disadvantaged groups that have never previously applied for, secured, or repaid loans or mortgages.

And in most cases, when an algorithm makes a decision, it is difficult for anyone to understand how it came to that conclusion, resulting in what is commonly referred to as black-box syndrome. It means that banks, for example, might struggle to explain what an applicant could have done differently to qualify for a loan or credit card, or whether changing an applicants gender from male to female might result in a different outcome.

Circiumaru said the AI act, which could come into effect in late 2024, would benefit tech companies that managed to develop what he called trustworthy AI models that are compliant with the new EU rules.

Darko Matovski, the chief executive and co-founder of London-headquartered AI startup causaLens, believes his firm is among them.

The startup, which publicly launched in January 2021, has already licensed its technology to the likes of asset manager Aviva, and quant trading firm Tibra, and says a number of retail banks are in the process of signing deals with the firm before the EU rules come into force.

The entrepreneur said causaLens offers a more advanced form of AI that avoids potential bias by accounting and controlling for discriminatory correlations in the data. Correlation-based models are learning the injustices from the past and theyre just replaying it into the future, Matovski said.

He believes the proliferation of so-called causal AI models like his own will lead to better outcomes for marginalised groups who may have missed out on educational and financial opportunities.

It is really hard to understand the scale of the damage already caused, because we cannot really inspect this model, he said. We dont know how many people havent gone to university because of a haywire algorithm. We dont know how many people werent able to get their mortgage because of algorithm biases. We just dont know.

Matovski said the only way to protect against potential discrimination was to use protected characteristics such as disability, gender or race as an input but guarantee that regardless of those specific inputs, the decision did not change.

He said it was a matter of ensuring AI models reflected our current social values and avoided perpetuating any racist, ableist or misogynistic decision-making from the past. Society thinks that we should treat everybody equal, no matter what gender, what their postcode is, what race they are. So then the algorithms must not only try to do it, but they must guarantee it, he said.

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While the EUs new rules are likely to be a big step in curbing machine-based bias, some experts, including those at the Ada Lovelace Institute, are pushing for consumers to have the right to complain and seek redress if they think they have been put at a disadvantage.

The risks posed by AI, especially when applied in certain specific circumstances, are real, significant and already present, Circiumaru said.

AI regulation should ensure that individuals will be appropriately protected from harm by approving or not approving uses of AI and have remedies available where approved AI systems malfunction or result in harms. We cannot pretend approved AI systems will always function perfectly and fail to prepare for the instances when they wont.

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Risks posed by AI are real: EU moves to beat the algorithms that ruin lives - The Guardian

CHICAGO (PRWEB) August 09, 2022

The League for Innovation in the Community College today announced a new initiative that will provide institutions and instructors with instructional technology powered by artificial intelligence (AI), along with faculty development resources and ongoing research, to further student retention.

Created through a unique partnership with Packback, builders of AI-enabled digital writing tutors for students and digital grading assistants for faculty, the initiative will pair Packbacks popular instructional technology with League-sponsored professional development opportunities, including badges awarded to faculty that recognize exceptional instructor engagement.

As remote and hybrid learning become the new normal, we have a unique opportunity to explore how new tools and strategies can help instructors spend less time on rote tasks like grading and more time providing our students with substantive support, said Kathleen D. Borbee, Professor of Business Administration at Monroe Community College. We know that written discussions and cases can play a significant role in developing students' critical thinking skills while keeping them engaged and motivated. I am excited that the League can give me the tools as an instructor to better meet the needs of my students.

The initiative stems from the work of the Leagues recent project Faculty Voices, which identified challenges that community college faculty face in their efforts to help more students complete their education. Recent Faculty Voices research found that community college instructors are interested in conducting more high-impact practices that could improve student retention but are held back by how time- and resource-intensive those practices are. In response to that challenge, the League will provide instructors with technology through this partnership with Packback, as well as the professional development and recognition to help them drive meaningful improvements to student success.

Our faculty are immensely important keys to improving student success, and with the right support systems can help students achieve even greater learning. We are interested in researching how reimagining faculty time can unlock more high-impact feedback to have a profound effect on student performance with our participating colleges, said Dr. Cynthia Wilson, Vice President for Learning and Chief Impact Officer at the League for Innovation. The Packback teams commitment to rigorous, research-backed pedagogical approaches have made them a valuable partner in our effort to provide community college instructors with the resources to lead engaging class experiences.

***

About Packback:Packback is a digital writing tutor for every student, along with a digital grading assistant for every teacher. A recent study conducted in partnership with 10 community colleges indicates that students in classes that use Packback received more A, B, C grades and fewer D, F, W's than the control group, and also cited sources approximately 2.5 times as often as students in the control group.

About The League for Innovation in the Community College:The League for Innovation in the Community College is an international nonprofit organization with a mission to cultivate innovation in the community college environment. The League serves as a catalyst for introducing and sustaining deep, transformational innovation within and across colleges and international borders to increase student success and institutional excellence. Founded in 1968 by B. Lamar Johnson and a dozen U.S. community and technical college presidents, the League has proudly served community college institutions for over 50 years. Through these years, the League has sponsored more than 200 conferences, institutes, seminars, and workshops; published over 200 reports, monographs, periodicals, and books; led more than 160 research and demonstration projects; and provided numerous other resources and services to the community college field.

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Nationwide Initiative Will Help Community College Faculty Utilize Artificial Intelligence to Improve Student Success - PR Web

August 9, 2022 Bot Image, anOmaha-based MRI medical device company has developed an AI-driven medical device CAD software to significantly improve the accuracy and speed ofprostate cancerdetection (CADe) and diagnosis (CADx). The tool, calledProstatID, combines artificial intelligence with traditional MRI scanning.

"Prostate cancer screening and detection methods adoption has changed little over the past 30 years, despite the mountain of evidence pointing to the efficacy of superior technologies and the futility of the old methods," says the company founder and CEO Dr.Randall W. Jones. "Sadly, this has resulted in the unnecessary and premature deaths of countless numbers of men in the US alone. ProstatID represents an exciting step in the fight to save lives."

Trained by analyzing thousands ofMRIimage sets, radiological interpretations, guided biopsies, and pathology lab results, the software's algorithm recognizes and measures the volume of the prostate gland, and detects suspicious cancerous lesions - assigning a cancer probability to each one, and suggests a diagnostic case score known as PI-RADS (Prostate Imaging Reporting and Data Sytem) score. This was proven in two clinical studies (involving 25 radiologists from around the US) to significantly improve radiologic interpretation accuracy as measured by improved detection and fewer false positives when radiologists used the aid of the CAD. This demonstrates a tremendous savings of time and cost for the patient and provider, potentially saving lives in the process via early and accurate detection.

ProstatID's ability to detect lesions and assign a cancer probability to prostate MRI cases goes far beyond existing technologies which have improved on-screen formatting of prostate MRI cases and segmentations of a patient's prostate but stopped short of aiding in detection and diagnosis.

The computer-aided design tool is currently available for use as a Software-as-a-service (SaaS) device which requires only a secure VPN tunnel connection between the radiology department server or MRI system and the cloud-based ProstatID server. The software and connectivity are both HIPAA compliant and cyber-secure.

"With FDA clearance, the path for implementation is open", says Jones, "and hospitals and radiological clinics can connect in as little as one hour of IT effort, and begin bringing this exciting technology to their patients".

For more information:https://www.botimageai.com/

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Bot Image, Receives FDA Clearance for Artificial Intelligence Software Used in Detection and Diagnosis of Prostate Cancer - Imaging Technology News

This is a guest post by Saloni Bogati of quantinsti.com.

Latest technological phenomena like ML and AI transform businesses, industries, and their scope of growth. The finance industry is notorious for using the latest technologies and solutions to achieve objectives.

Trading has also seen its fair share of advancements in recent years and has yielded the abilities of these technologies. There is also a significant increase in the use of AI and ML techniques to build trading systems using data. Further, Artificial Intelligence and Machine Learning enhance trading efficiency by using innovative solutions.

Hence, in this article, we will learn:

Artificial Intelligence or AI is a computer science stream that develops machines capable of imitating the human mind. In other words, it enables devices to think and react like humans to perform specific tasks without human intervention.

For example, virtual assistants like Siri, Alexa, and Google Assistant are a part of our daily lives. Virtual assistants often use data history, voice technology, and other features to make our lives easier.

Machine Learning is a subject of Artificial Intelligence that enables software solutions to make decisions based on accurate and calculated information. It develops machine capabilities to use algorithms to duplicate the way humans learn.

It also progressively enhances reliability and accuracy by analyzing historical data to procure desired results. Moreover, ML makes the machines more human by training the devices with the ability to learn and develop.

The evolution of trading defines the development and journey of humans. Trading denotes the system of equally exchanging products, services, and money to gain specific ownership. Earlier, the barter system was a popular method of determining exchange. Further, with the inception of coins and money, the manner of exchange evolved. Soon, trading evolved as coins and currencies emerged to define the value of products, commodities, and services. Automated Trading accounted for about 70% of US equities in 2013. Algorithmic trading accounted for a third of the total volume on Indian cash shares and almost half of the book in the derivatives segment. Hence, trading has evolved from the early days of yore to the recent technological development. Therefore, terms like algo trading are now gaining momentum as the present leads the lives of future traders.

AI and ML have a tremendous impact on trading. Therefore, the reasons why AI and ML are pivotal in Trading:

According to a report by Allied Market Research, The global AI and advance machine learning in BFSI market size was valued at $7.66 billion in 2020, and is projected to reach $61.24 billion by 2030, growing at a CAGR of 23.1% from 2021 to 2030.

Artificial Intelligence and machine learning in trading offer financial industry solutions that help streamline various processes. It also helps in optimizing decisions in quantitative trading and manages financial risks. Hence, the solutions and services offered by AI and ML help automate processes in trading and reduce manual and repetitive tasks. Hence, here are different ways in which AI and ML contribute to the world of trading:

AI and ML often use abilities like neural networks and other learning models to detect and analyse factors that influence stock prices. That is to say; the factors act as predictors or features that help determine the future of stocks. For instance, AI can help detect technical, social, economic, demographical, and other factors to gain desired results. Therefore, traders can use these insights and knowledge to develop robust ML algorithms, strategies, and models to trade.

Artificial Intelligence uses automated systems that perform tasks based on facts and the accuracy of the information. On the other hand, humans may make specific errors based on emotions, cloud judgments, agendas, etc. Therefore, AIs fact-based decision-making process offers optimum results for participants.

AI in trading also increases the need for human management as an organisation is now looking for experts in Mathematics, Computer Programming, etc., to develop strategies. As a result, AI improves decision-making processes while experts develop ML strategies for various trading agendas.

Chatbots are virtual assistants that enable traders to find easy solutions. It also mitigates the use of agents for mundane queries. Moreover, traders can access chatbots anytime in the day as it does not require human intervention and assist questions using automated responses.

AI helps predict stock prices using the factors that influence the market. Therefore, it can use similar elements and data to anticipate risks and enable ML algorithms to avoid scenarios or mitigate actions that may lead to the risk. Further, AI can process large sets of data rapidly and accurately. Machine Learning can help replicate the scenarios within the models and learn various techniques to optimize the results. Hence, AI can be the brain of the operation, and ML is the limb that follows instructions while learning and developing its abilities.

Artificial Intelligence and Machine Learning play a pivotal role in trading by offering rapid and simplified solutions. The technologies enhance the processes of innovating and modernizing the various concepts in trading. Therefore, the following are some revolutionizing applications of AI and ML in trading:

Sentiment Analysis is a general application of Machine Learning in the financial market. It helps analyze larger volumes of data related to assets and other investment information. Machine learning also leverages NLP (Natural language processing) in trading to rapidly and accurately analyse various data sets. Therefore, it is critical to have a comprehensive understanding of Sentiment analysis in Financial markets.

Machine Learning also helps improve sentimental analysis in the following categories according to requirements:

Traders often inquire about the estimated results of their trades. Although it is tedious to predict an accurate outcome, ML can help understand the factors that may affect the desired result. Therefore, traders can use these estimates to analyze possible results using research insights and probability.

Data fuels the engine for AI and Machine Learning. It is vital to have large volumes of data sets to develop ML algorithms and models. Therefore, another ML application in trading is developing synthetic data. Moreover, Generative Adversarial Networks (GANs) can help counter challenges like data scarcity, data privacy, data costs, backtesting overfitting, etc.

According to Coherent Market Insights, Global algorithmic trading market was valued at US $10,346.6 Mn in 2018 and is expected to exhibit a CAGR of 10.7% over the forecast period to reach US$ 25,257.0 Mn in 2027.

Therefore, the expanding market demands AI and ML-based solutions to meet the expectations. Machine learning can improve the speed of search for efficient Algo Trading Strategies. It also helps traders optimize their desired results and simulate risks while trading. ML and AI can also integrate their abilities with various algorithmic trading platforms to help investment professionals. For instance, multiple techniques employ ML and AI to enhance algorithms, including neural networks, deep learning, linear regressions, etc., often covered in machine learning courses.

Traders often analyze the opportunities and risks associated with trading stocks. They also want to predict the future value of specific stocks. Therefore, AI and ML strategies enable systems to make estimations based on real-world data. The strategies analyze various scenarios and factors affecting the desired outcome and provide information based on the calculations.

Moreover, a trader must always hope for the best yet prepare for the worst-case scenario by identifying and assessing the risks. ML algorithms can analyze large data sets and offer insights based on calculations and impact.

Programming Languages like Python in Trading

Machine Learning and Python have become widespread integration into algorithmic trading. Moreover, Python codes are easy to read and comprehend with extensive libraries. As a result, including programming languages in AI and ML strategies opens up several avenues for trading. It also offers robust computing power to enable scalability. Therefore, programming languages simplify processes by using comprehensive libraries for trading.

In the world of trading, AI and ML are actively used in Algorithmic Trading by various organizations and retail investors. Moreover, the concept help develops algorithms that comprehend market conditions, learn from past data, make calculated decisions, etc.

The use of AI and ML in trading is algorithmic or automated solutions that integrate AI analysis, self-developing algorithms, managing tasks according to trading agendas, etc. Therefore, it can create an environment for traders and investors to use solutions that offer optimum results optimally. As technology evolves, traders and investors must upgrade their skills to the leverage abilities of technologies. An algorithmic trading course guides traders and investors with the help of industry experts and updated training modules.

You can follow QuantInsti on Twitter here an check out more information on their website at quantinsti.com.

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Artificial Intelligence and Machine Learning in Trading: How are they changing the world of Trading? - New Trader U