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Harnessing Machine Learning for Accurate Weather Predictions: A New Dawn for Developers

Artificial Intelligence & Machine Learning

In the vast realm of technology, weather prediction has always posed a unique challenge. The unpredictability of Mother Nature, combined with the intricate variables at play, makes forecasting a complex endeavor. However, with the advent of machine learning, a transformative shift is on the horizon. Developers, this is your moment to shine and reshape the future of meteorology.

Imagine a world where weather predictions are not just accurate but also tailored to specific needs, from agriculture to event planning. Tomorrow.io, a pioneer in weather technology, has made significant strides in this direction. Their R&D teams achievement, as they put it, leveraged an approach powered by physical models and supercharged with AI/ML, allowing for vastly improved decision-making confidence in advance of weather impact. For developers, this is a testament to the limitless possibilities that AI and ML hold. (source: Tomorrow.io)

At the heart of this revolution is the 1F Model. Its not just a forecasting tool; its a beacon of innovation that combines machine learning with numerical weather prediction technology. The result? Predictive data thats up to 38% more reliable than other forecasts. Developers, think about the applications! From smart homes adjusting heating based on accurate weather predictions to farmers receiving real-time updates for optimal crop yield the opportunities are boundless.

Diving deeper, the 1F Model stands out due to its high-resolution, short-term forecasting system. It leverages a unique blend of machine learning and state-of-the-art numerical weather prediction technology. As Luke Peffers, Chief Weather Officer at Tomorrow.io, aptly states, Our next-generation 1F model is a game-changer We are pushing the boundaries of what can be reliably predicted through a combination of NWP models and machine learning. For the developer community, this is both a challenge and an invitation to innovate.

The proof, as they say, is in the pudding. The 1F Model has demonstrated remarkable results, outperforming baseline models by up to 12.5%. But whats truly groundbreaking is its probabilistic forecasting, which has shown a staggering 38% improvement. Tyler McCandless, Director of Data Science at Tomorrow.io, emphasizes, We demonstrated a tremendous improvement in probabilistic forecasting Developers, this is a clarion call to harness the power of AI and redefine industries.

The horizon looks promising. The fusion of high-resolution NWP with deep learning is set to usher in an era of unparalleled accuracy in weather forecasting. Developers have the tools, the technology, and now, proven models to inspire their creations. Whether its building apps that help cities manage traffic during storms or software that aids in disaster preparedness, the skys the limit.

The intersection of technology and meteorology offers a fertile ground for innovation. As the digital age progresses, the role of developers becomes increasingly pivotal. With tools like AI and machine learning, developers arent just solving technical challenges; theyre addressing global issues, enhancing safety, and improving daily lives. Embracing the potential of AI-driven weather predictions isnt merely about advancing technology; its about crafting a future where humanity is better prepared, more resilient, and deeply connected to the world around us.

Developers, you hold the key to this transformative journey. Lets shape a future thats not just predictable, but also brighter for all.

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Harnessing Machine Learning for Accurate Weather Predictions: A ... - Rebellion Research

Mr. Abhishek Banerjee, Founder & CEO, Lotusdew Wealth and Investment Advisors

In the fast-paced world of finance and investments, staying ahead of the curve is essential for successful portfolio management. To accomplish this, investment firms are increasingly turning to the transformative powers of Artificial Intelligence (AI) and Machine Learning (ML). These technologies are redefining portfolio management by providing novel answers to age-old dilemmas. Portfolio managers can unlock new opportunities and obtain a competitive edge through advanced analytics, predictive insights, and data-driven decisions by harnessing the power of these technologies.

Leveraging AI and Machine Learning have contributed to a wave of innovation in portfolio management, offering solutions that cover various aspects:

Risk Management: According to Statista, the global AI software market is projected to reach $126 billion by 2025. AI and ML algorithms excel at risk management by analyzing vast datasets to predict potential risks. These technologies can identify patterns and correlations that human analysts might overlook, providing portfolio managers with early warnings and insights into market volatility and economic indicators.

Asset Allocation: AI and ML empower portfolio managers to dynamically allocate assets in real-time, considering ever-changing market conditions. Gartner reports that 37% of organizations have implemented AI in some form, with a 270% growth in AI adoption over the past four years. These technologies optimize asset allocation strategies by continuously adapting to market trends and individual portfolio objectives.

Stock Selection: Machine learning models are trained on extensive datasets, including historical stock performance, economic indicators, and market sentiment. This data-driven approach enables investment professionals to make more informed decisions about stock selection.

Incorporating AI and ML in portfolio management offers a multitude of advantages:

Data-Driven Insights: AI and ML can process and analyze large volumes of data quickly and efficiently. According to Servion Global Solutions, by 2025, 95% of customer interactions will be powered by AI. This data-driven approach provides portfolio managers with invaluable insights that guide decision-making, uncover hidden patterns, and enhance portfolio performance.

Efficiency and Convenience: Automation is at the heart of AI and ML in portfolio management. These technologies automate tasks such as data analysis, portfolio optimization, and reporting, saving time and resources. Moreover, AI-based portfolio management tools offer real-time updates and alerts, accessible via web or mobile platforms. This level of efficiency and convenience is essential in today's fast-paced investment landscape.

While the potential benefits are significant, challenges must be addressed:

Data Quality: The accuracy and quality of data are crucial for training AI and ML models. Low-quality data can lead to biased or unreliable results. It is estimated that 80% of the work in AI projects involves data preparation, highlighting the importance of high-quality data.

Reliability and Accuracy: AI systems are not infallible and can make mistakes. The reliability and accuracy of AI-driven decisions can be influenced by various factors, including data quality and external market dynamics. It is imperative to have human oversight and critical evaluation of AI-driven insights.

Transparency and Trust: AI algorithms can be complex and opaque, making it challenging to explain their decisions. To build trust between investors and AI-based portfolio management tools, transparency, clear communication, and adequate control mechanisms are essential.

Exploring real-world examples of how AI and ML are shaping corporate portfolio management:

In a nutshell, AI and ML technologies are reshaping the corporate portfolio management landscape. As these technologies continue to develop, they hold the potential to enhance investment strategies and increase returns for investors worldwide. AI and ML are increasingly relied upon by today's investment industries to provide quality and exceptional customer service. The majority of finance executives view technology as an enabler and anticipate a positive return on AI investments. It's time to investigate game-changing AI solutions in order to achieve remarkable development. Commence immediately!

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Unveiling the ability of machine learning and AI to shape corporate ... - Dalal Street Investment Journal

An influential group of researchers is making the case for new ways to search the skies for signs of alien societies. They argue that current methods could be biased by human-centered thinking, and that its time to take advantage of data-driven, machine learning techniques.

The team of 22 scientists released a new report on August 30, contending that the field needs to make better use of new and underutilized tools, namely gigantic catalogs from telescope surveys and computer algorithms that can mine those catalogs to spot astrophysical oddities that might have gone unnoticed. Maybe an anomaly will point to an object or phenomenon that is artificialthat is, alienin origin. For example, chlorofluorocarbons and nitrogen oxide in a worlds atmosphere could be signs of industrial pollution, like smog. Or perhaps scientists could one day detect a sign of waste heat emitted by a Dyson spherea hypothetical massive shell that an alien civilization might build around a star to harness its solar power.

We now have vast data sets from sky surveys at all wavelengths, covering the sky again and again and again, says George Djorgovski, a Caltech astronomer and one of the reports lead authors. Weve never had so much information about the sky in the past, and we have tools to explore it. In particular, machine learning gives us opportunities to look for sources that may be inconspicuous but, in some waywith different colors or behavior in timethey stand out. For example, that could include objects that flicker or are surprisingly bright at some wavelength, or ones that move unusually fast or orbit in an unexplainable path.

Of course, most of the time, data outliers turn out to have mundane explanations, like an instrumental error. Sometimes they do reveal novelties, but of a more astrophysical nature, like a type of variable star, quasar, or supernova explosion no one has seen before. Thats a crucial advantage of this approach, the scientists argue: No matter what happens, they always learn something. The report quotes astrophysicist Freeman Dyson: Every search for alien civilizations should be planned to give interesting results even when no aliens are discovered.

The project grew out of a major 2019 workshop at Caltechs Keck Institute for Space Studies in Pasadena, California, and includes a team of astronomers and planetary scientists primarily at Caltech and NASAs Jet Propulsion Laboratoryplus a handful of others, like Jason Wright from Penn States Center for Exoplanets and Habitable Worlds, and Denise Herzing, an expert on dolphin communication, who was included because of her expertise on nonhuman languages.

The hunt for alien technosignatures is related to, but differs from, astrobiology, which often refers to the broader search for habitablenot necessarily inhabitedplanets. Astrobiologists look for signs of the elements necessary for life as we know it, such as liquid surface water and atmospheres with the chemical signatures of oxygen, carbon dioxide, methane, or ozone. Their search typically includes seeking evidence of very simple life forms, such as bacteria, algae, or tardigrades. The James Webb Space Telescope has helped astronomers make headway there, by enabling spectroscopy of planetary atmospheres and illuminating promising worlds like K2-18 b, which has methane and carbon dioxide, and GJ 486 b, which appears to have water vapor.

See more here:
Scientists Say You're Looking for Alien Civilizations All Wrong - WIRED

This comprehensive market research report thoroughly analyses the Machine Learning in Education Market exploring the factors driving its growth, the obstacles it faces, and the opportunities it presents. It delivers an unbiased evaluation of the markets performance, highlighting the latest industry advancements and innovative practices. Furthermore, the report examines the competitive landscape, including the strategies employed by key players, and identifies promising growth prospects in both established and emerging segments and regions. By offering a historical, current, and projected market size in terms of value, the report provides a detailed assessment of the Machine Learning in Education Market. It also offers a regional perspective that provides valuable insights into the markets performance across different geographical areas.

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The COVID-19 pandemic has adversely affected several industries, including the Machine Learning in Education market, across the globe. The global Machine Learning in Education Market is expected to Maximize by the end of 2031, Growing at a Significant CAGR During 2023-2031.

List of TOP KEY PLAYERS in Machine Learning in Education Market report are:

-- IBM-- Microsoft-- Google-- AWS-- Cognizant-- Pearson-- Bridge-U-- DreamBox Learning-- Fishtree-- Jellynote-- Quantum Adaptive Learning-- Nuance Communications-- OSMO-- Querium-- Third Space Learning-- Aleks-- Blackboard-- Carnegie Learning-- Century-- Cognii-- Elemental Path-- Jenzabar-- Knewton-- Luilishuo-- Metacog

Machine Learning in Education Market Segment Analysis:

The Machine Learning in Education Market Forecast report provides a holistic evaluation of the market. The report offers a comprehensive analysis of key segments, trends, drivers, restraints, competitive landscape, and factors that are playing a substantial role in the market. The Machine Learning in Education market segments and market data breakdown are illuminated.

Machine Learning in Education Market, By Type:

-- Deep Learning and Machine Learning-- Natural Language Processing (NLP)

Machine Learning in Education Market, By Application:

-- Intelligent Tutoring Systems-- Virtual Facilitators-- Content Delivery Systems-- Interactive Websites-- Others

Machine Learning in Education Market Regional Analysis:

The research study covers North America, Latin America, Asia-Pacific, Europe, Middle East and Africa on the basis of productivity, thus focusing on the leading countries from the global regions. The report further highlights the cost structure including cost of raw material and cost of manpower. It offers cogent analysis of business stimulants of the Machine Learning in Education market

Years Considered for the Machine Learning in Education Market:

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A comprehensive market research report delves deeply into the market of interest, providing an in-depth analysis of its key drivers, barriers, and growth prospects. It examines the factors influencing market demand, such as changing consumer preferences, technological advancements, economic conditions, and regulatory policies. By understanding these drivers, businesses can adapt their offerings and marketing strategies to meet customer needs effectively.

Machine Learning in Education Market Key Points:

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COMTEX_440189904/2599/2023-09-14T06:56:11

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Global Machine Learning in Education Market to Showcase Dynamic Demand during the Upcoming Years, Forecas - Benzinga

- Vast high-throughput screening study used to train unique machine learning algorithm to design synthetic activators -

- Rational engineering produced activators that induce the most durable and mitotically stable gene activation reported to date -

SOUTH SAN FRANCISCO, Calif., Sept. 14, 2023 (GLOBE NEWSWIRE) -- Epic Bio, a biotechnology company developing therapies to modulate gene expression using compact, non-cutting dCas proteins, today announced data supporting the breakthrough potential of its Gene Expression Modulation System (GEMS) platform for epigenetic engineering. In two preprint studies posted onbioRxiv, the company reported the discovery of exceptionally durable, hypercompact gene activators, and the training of a machine learning model to generate additional synthetic activators. Epic Bio, a biotechnology company developing therapies to modulate gene expression using compact, non-cutting dCas proteins, today announced data supporting the breakthrough potential of its Gene Expression Modulation System (GEMS) platform for epigenetic engineering. In two preprint studies posted onbioRxiv, the company reported the discovery of exceptionally durable, hypercompact gene activators, and the training of a machine learning model to generate additional synthetic activators.

Novel Activators from High-Throughput Screen are Optimized

In the first paper, Discovery and engineering of hypercompact epigenetic modulators for durable gene activation, Epic Bios team reports the outcomes of the widest-known survey of naturally occurring protein sequences to identify novel activators, and subsequent engineering to overcome the primary barriers to therapeutic use.

Epic scientists designed a high-throughput screen to systematically integrate the transcriptional effects of peptide sequences from human, viral and archaeal species. The peptide sequences were incorporated into a GEMS construct and assayed for their ability to activate a synthetic genetic locus.

Resulting activators were then subjected to protein engineering to overcome the three main barriers to therapeutic use: Activator potency (strength of activation), robustness (activity against multiple different target types), and durability (persistence/heritability of the gene activation after transient delivery of the activator).

Ultimately, Epics team created activators that induce the most durable and mitotically stable gene activation reported to date. These display a novel ability to maintain target activation through dozens of cell divisions after a single transient delivery, despite occupying ~12-20% of the cargo size of the currently most commonly used activators.

Machine Learning Improves Success in Discovering Novel Activators

In the second paper, Improving few-shot learning-based protein engineering with evolutionary sampling, Epic reports on the development of a novel machine-learning approach, trained on the activators discovered in its prior work, to design entirely new synthetic activators. Improving few-shot learning-based protein engineering with evolutionary sampling, Epic reports on the development of a novel machine-learning approach, trained on the activators discovered in its prior work, to design entirely new synthetic activators.

To address the challenges of limited training data and the rarity of positive hits in this setting, Doctors Zaki Jawaid, Robin Yeo, and Timothy Daley created a novel Evolutionary Monte Carlo algorithm, called Evolutionary Monte Carlo Search, to efficiently sample the fitness landscape and propose novel, potent gene activators. Proposed activator sequences were experimentally validated for their ability to activate a synthetic genetic locus.

Researchers found that Evolutionary Monte Carlo Search was not only capable of improving the sequence diversity and novelty of designed sequences, but that it dramatically improved the hit rate of finding functional gene activators, both compared to more traditional machine-learning approaches as well as compared to the outputs of high-throughput screening.

This approach therefore holds promise for a number of diverse protein engineering challenges, and has the potential to accelerate the design of novel and active proteins for a variety of purposes including therapeutics.

About Epic Bio Epic Bio is a leading epigenetic editing company, leveraging the power of CRISPR without cutting DNA. The companys proprietary Gene Expression Modulation System (GEMS) includes the smallest Cas protein known to work in human cells, enabling in vivo delivery via a single AAV vector. Epics lead program, EPI-321, is in IND-enabling studies for treatment of facioscapulohumeral muscular dystrophy (FSHD); additional programs seek to address alpha-1 antitrypsin deficiency (A1AD), heterozygous familial hypercholesterolemia (HeFH), and other indications. Visitwww.epic-bio.com for more information or follow us onTwitterandLinkedIn.

Investor Contact

Shawn M. Cox Epic Bio Manager, Investor Relations and Corporate Communications shawn.cox@epic-bio.com

Media Contact

Lisa Raffensperger Ten Bridge Communications lisa@tenbridgecommunications.com (617) 903-8783

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Epic Bio Reports Discovery of Exceptionally Durable Gene ... - GlobeNewswire