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Pagaya Technologies: Massive Potential Ahead (NASDAQ:PGY)

#artificialintelligence

Pagaya Technologies (NASDAQ:PGY) recently completed its business combination with EJF Acquisition Corp. and became a public company with a valuation of around $8 billion. The company has yet to report any formal earnings but I will talk about why I like its prospect in this article. Pagaya is an Israeli company founded in 2016 by the current CEO Gal Krubiner. It is a fintech company that is revolutionizing how financial companies approve and recruit consumers by utilizing AI (artificial intelligence). It enables accurate, real-time consumer credit evaluation through the use of robust AI-driven credit and analytical technology.


Top 5 Best Apple Watch Uses, Tips & Tricks To Get The Most Out Of Smartwatch

#artificialintelligence

Apple Watch is one of the best-designed smartwatches out there and it provides you with a lot more functionality than just tracking your activities and showing time. There are a lot of hidden Apple watch tips and tricks that you may not know about, so here’s a guide on how to enable them. Ease of use is often the focus for many of Apple’s consumer products and Apple Watch is not that different. The Apple Watch packs a lot of functions and you can actually use it for things other than checking the time or tracking your workouts. For most of the part, you may be familiar with many basic functions on the watch but there are some hidden features and recent improvements that enable you to get more out of your watch.  Here are some Apple Watch Uses, tips and tricks that you should try: Continue Reading . . . . . . . . . . . . . . . . . . . . . . . . Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK), Apple (AAPL), Wearable Tech, Technology, Gear&gadgets, S&P 500, Corporate Finance, Nasdaq, iPhone, Apple Music, Smartwatches, Wearable Tech, Investing, Stock Markets, Phones, Financial Markets, App Stores, iOS Apps, Capital Markets, Dow Jones Industrial Average, Warren Buffett, Apple Watch, Stocks, iOS, Mobile Payments, iPad, Microsoft, iPhone Apps, Trading, Apple (AAPL), Emerging Technology, Tech Trends, Artificial Intelligence, Innovation, Technology (Israel), Machine Learning, Technology (China), Education Technology, Computer Science, Big Data, Internet of Things, Problem-solving, Augmented Reality, E-Learning, Technology (Australia), Technology (Africa), Startups, Business Technology, Technology (New Zealand), Robotics, Virtual Reality, Analytics, Technology, Technology (India), Technology (UK)


Is Russia's Largest Tech Company Too Big to Fail?

WIRED

It was February 11, his birthday, and the 58-year-old billionaire CEO and cofounder of Yandex, the Russian tech behemoth, was in the sort of open, engaging mood that could be called privetliviy, after the casual Russian word privet for hello. He was speaking from his car in Tel Aviv, bragging about his father--an oil geologist in his eighties who had "discovered" oil in Israel, Volozh said--as we chatted about my upcoming trip to Tel Aviv to interview him for this story. For more than 20 years, Yandex has been known as "Russia's Google": It began as a search engine in 1997 and still has a 60 percent share of the Russian search market. But for the past decade, this tag has understated the company's inescapable ubiquity in Russians' daily life. Yandex Music is the country's leader in paid music streaming, and Yandex Taxi is the top ride-hailing app.


"The future of business lies in AI-based platforms"

#artificialintelligence

'Our operating model is high-touch," says Gideon Argov, managing partner at New Era. The Israel-US venture capital fund recently announced a $140 million second investment fund and is looking toward a third fund to leverage its latest success. New Era's funds are already valued at more than $500m. Established in 2017 by Argov and Ran Simha, the fund focuses on early-stage investments in Israeli start-ups that use breakthrough technologies, emphasizing artificial intelligence and machine learning."We "We think that Israeli companies should not be sold prematurely. They need to become established outside Israel to realize their full potential and develop a global reach. "It is important that they attain this regardless of whether they become public companies.


Counterfactual Memorization in Neural Language Models

arXiv.org Artificial Intelligence

Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.


Why AI is so difficult to apply in finance

#artificialintelligence

The issue of data quality is foremost in the financial sector. In the financial world, abundance of data is not an issue. Data can easily be collected from a wide variety of sources such as instrument prices, news articles, stock fundamentals, social media posts, macroeconomic data, ESG data, credit card transactions, and so on. Some of this data is classified as structured and typically has a numerical quantity and a well-defined structure (e.g. stock prices). Structured data is relatively easy to feed into an ML model whereas unstructured data often requires extra processing to extract meaningful information (e.g.


Tel Aviv based Regtech Shield Introduces Alert Transparency Capabilities by Leveraging AI, NLP

#artificialintelligence

The team at Tel Aviv-based Shield, an established Regtech firm, reveals that they're introducing their powerful Alert Transparency capabilities, bringing "unmatched" understanding to compliance alerts and triggers via AI, Natural Language Processing (NLP), and various other backend technologies. As regulatory authorities throughout the world aim to define and understand AI's growing role within financial institutions and AI-powered Fintech companies struggle to provide "true" transparency due to their proprietary "black box" solutions, Alert Transparency offers key insights into "why an alert was triggered so financial organizations can detect possible market manipulations, which has been particularly prevalent in today's new hybrid work from home environment." "With the already proven ability to automate surveillance through its award-winning artificial intelligence platform, Shield's Alert Transparency provides compliance teams with an in-depth analysis and understanding of communication triggers, including the scenario, the rule that was compromised, and an overall relevancy score." Since regulatory guidelines and related procedures tend to vary based on the specific financial organization, as well as how and where they do business, compliance officers are able "to customize what triggers an alert to the specific needs of their company," the announcement from Shield noted. A complete Workplace Intelligence platform, Shield's Alert Transparency is able "to pinpoint various risks across communication channels, including insider trading, spoofing, front-running, and even sexual harassment and racism," the update from Shield explained.


ZoomInfo to Acquire Conversation Intelligence Leader Chorus.ai to Enable Insight-Driven Targeting, Coaching, and Decision-Making for Go-to-Market Teams

#artificialintelligence

VANCOUVER, Wash.--(BUSINESS WIRE)--ZoomInfo (NASDAQ: ZI), a global leader in modern go-to-market software, data, and intelligence, today announced it has agreed to acquire Chorus.ai, More than 20,000 global revenue teams trust ZoomInfo to power their go-to-market motions and drive efficient results. The planned acquisition of Chorus will add a new category of actionable insights to ZoomInfo's world-class intelligence layer, unlocking workflows and driving engagement informed by conversations. The acquisition expands ZoomInfo's total addressable market to $70 billion, and is expected to be accretive to growth immediately, generate positive adjusted operating income within 12 months, and be accretive to cash flow in the second half of FY 2022. Chorus uses machine learning and artificial intelligence to capture and analyze prospect and customer calls, meetings, and emails.


Artificial Intelligence (AI) in Construction Market SWOT Analysis by Size, Status and Forecast to 2021-2027 - The Manomet Current

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Latest published market study on Global Artificial Intelligence (AI) in Construction Market provides an overview of the current market dynamics in the Artificial Intelligence (AI) in Construction space, as well as what our survey respondents--all outsourcing decision-makers--predict the market will look like in 2027. The study breaks market by revenue and volume (wherever applicable) and price history to estimates size and trend analysis and identifying gaps and opportunities. Some of the players that are in coverage of the study are Renoworks Software, SmarTVid.Io, Jaroop, Smartvid.io, Get ready to identify the pros and cons of regulatory framework, local reforms and its impact on the Industry. Market Factor Analysis: In this economic slowdown, impact on various industries is huge.


Use Machine Learning and GridDB to build a Production-Ready Stock Market Anomaly Detector

#artificialintelligence

In this project, we use GridDB to create a Machine Learning platform where we Kafka is used to import stock market data from Alphavantage, a market data provider. Tensorflow and Keras train a model that is then stored in GridDB, and then finally uses LSTM prediction to find anomalies in daily intraday trading history. The last piece is that the data is visualized in Grafana and then we configure GridDB to send notifications via its REST Trigger function to Twilio's Sendgrid. The actual machine learning portion of this project was inspired by posts on Towards Data Science and Curiously. This model and the data flow is also applicable to many other datasets such as predictive maintenance or machine failure prediction or wherever you want to find anomalies in time series data.