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Category: finance – Page 24

Cryptocurrency is usually “mined” through the blockchain by asking a computer to perform a complicated mathematical problem in exchange for tokens of cryptocurrency. But in research appearing in the journal Chem a team of chemists has repurposed this process, asking computers to instead generate the largest network ever created of chemical reactions which may have given rise to prebiotic molecules on early Earth.

This work indicates that at least some primitive forms of metabolism might have emerged without the involvement of enzymes, and it shows the potential to use blockchain to solve problems outside the financial sector that would otherwise require the use of expensive, hard to access supercomputers.

“At this point we can say we exhaustively looked for every possible combination of chemical reactivity that scientists believe to had been operative on primitive Earth,” says senior author Bartosz A. Grzybowski of the Korea Institute for Basic Science and the Polish Academy of Sciences.

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Bluesheets, an AI-powered financial data startup based in Singapore, announced Tuesday it raised $6.5 million in a Series A funding round led by fintech-focused VC Illuminate Financial, bringing the four-year-old startup’s total funding to $12.5 million.

Participating in the round were returning investors Insignia Ventures Partners, Antler Elevate–the emerging growth fund of VC firm Antler–and 1982 Ventures. The Series A values the startup at $30 million.

“A lot of organizations are trying to implement [AI] applications, but are struggling quite a lot,” says Luca Zorzino, general partner and head of Asia at Illuminate Financial, in a video interview. “What we liked about Bluesheets is that not only have they implemented AI themselves, but they can actually form the foundational layer for more AI adoption in financial services.”

Founded in 2020, Bluesheets develops AI-powered data entry and management tools that aim to help companies process their financial data for accounting, reporting and other operations. The startup claims it can update records in real-time while integrating with other enterprise software tools from Google, Microsoft, Quickbooks, Stripe and SAP.

Continue reading “Singapore Financial Data Startup Raises .5 Million Series A To Double Down On AI” | >

With the insertion of a little math, Sandia National Laboratories researchers have shown that neuromorphic computers, which synthetically replicate the brain’s logic, can solve more complex problems than those posed by artificial intelligence and may even earn a place in high-performance computing.

The findings, detailed in a recent article in the journal Nature Electronics, show that neuromorphic simulations employing the statistical method called random walks can track X-rays passing through bone and soft tissue, disease passing through a population, information flowing through social networks and the movements of financial markets, among other uses, said Sandia theoretical neuroscientist and lead researcher James Bradley Aimone.

“Basically, we have shown that neuromorphic hardware can yield computational advantages relevant to many applications, not just artificial intelligence to which it’s obviously kin,” said Aimone. “Newly discovered applications range from radiation transport and molecular simulations to computational finance, biology modeling and particle physics.”

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Thomvest Ventures is popping into 2024 with a new $250 million fund and the promotion of Umesh Padval and Nima Wedlake to the role of managing directors.

The Bay Area venture capital firm was started about 25 years ago by Peter Thomson, whose family is the majority owners of Thomson Reuters.

“Peter has always had a very strong interest in technology and what technology would do in terms of shaping society and the future,” Don Butler, Thomvest Ventures’ managing director, told TechCrunch. He met Thomson in 1999 and joined the firm in 2000.

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Many electric vehicles are powered by batteries that contain cobalt — a metal that carries high financial, environmental, and social costs.

MIT researchers have now designed a battery material that could offer a more sustainable way to power electric cars. The new lithium-ion battery includes a cathode based on organic materials, instead of cobalt or nickel (another metal often used in lithium-ion batteries).

In a new study, the researchers showed that this material, which could be produced at much lower cost than cobalt-containing batteries, can conduct electricity at similar rates as cobalt batteries. The new battery also has comparable storage capacity and can be charged up faster than cobalt batteries, the researchers report.

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When Taiwan Semiconductor Manufacturing Co. (TSMC) is prepping to roll out an all-new process technology, it usually builds a new fab to meet demand of its alpha customers and then either adds capacity by upgrading existing fabs or building another facility. With N2 (2nm-class), the company seems to be taking a slightly different approach as it is already constructing two N2-capable fabs and is awaiting for a government approval for the third one.

We are also preparing our N2 volume production starting in 2025,” said Mark Liu, TSMC’s outgoing chairman, at the company’s earnings call with financial analysts and investors. “We plan to build multiple fabs or multiple phases of 2nm technologies in both Hsinchu and Kaohsiung science parks to support the strong structural demand from our customers. […] “In the Taichung Science Park, the government approval process is ongoing and is also on track.”

TSMC is gearing up to construct two fabrication plants capable of producing N2 chips in Taiwan. The first fab is planned to be located near Baoshan in Hsinchu County, neighboring its R1 research and development center, which was specifically build to develop N2 technology and its successor. This facility is expected to commence high-volume manufacturing (HVM) of 2nm chips in the latter half of 2025. The second N2-capable fabrication plant by is to be located in the Kaohsiung Science Park, part of the Southern Taiwan Science Park near Kaohsiung. The initiation of HVM at this plant is projected to be slightly later, likely around 2026.

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The Galaxy S24 will also have a new feature called Circle to Search, which will let users search anything on their screen using Google. Users can press the bottom edge of the screen, where the Google logo and a search bar will pop up, and draw a circle around anything they want to search. The feature will work on most content, except for those protected by DRM or screenshots, such as banking apps. Once the selection is made, a panel will slide up showing the selection and the results from Google’s Search Generative Experience (SGE), similar to image search via Google or Lens, but without needing to open another app or take screenshots. Users will be able to circle items in YouTube videos, Instagram Stories, and more.

The Galaxy S24 will also benefit from Google’s Imagen 2, a text-to-image model that can generate realistic images from text descriptions. Imagen 2 will power the photo editing features in the Galaxy S24 Gallery app, such as the Generative Edit feature which also debuted on the Pixel 8 series. It can automatically fill in missing parts of images based on the surrounding context. Imagen 2 was unveiled at Google I/O last year and recently launched in preview on the web.

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The integration of Artificial Intelligence (AI) in lead generation is transforming how businesses identify and engage with potential customers.

Lead generation, a crucial aspect of business development, is undergoing a significant transformation thanks to AI. By leveraging machine learning, natural language processing, and predictive analytics, AI tools can identify prospective customers more accurately and engage them in a more personalized manner. This shift not only increases the volume of leads but also improves their quality, enabling businesses to focus their efforts on the most promising prospects, Einstein from Salesforce is a leader in customer relationship management (CRM), has integrated AI into its platform through Einstein. This AI-powered tool analyzes customer data to predict buying behaviors and recommend the most promising leads. For instance, a marketing agency used Einstein to prioritize leads based on their likelihood to convert, resulting in a 30% increase in sales productivity. HubSpot’s AI Lead Scoring: HubSpot offers an AI lead scoring system that ranks leads based on their potential value to the business. By analyzing historical data and user interactions, it helps companies focus their efforts on leads with the highest conversion potential. A technology startup reported a 25% increase in lead conversion rates after implementing HubSpot’s AI tool.

In addition, we have Drift’s AI Chatbots. Drift utilizes AI-powered chatbots to engage website visitors in real-time. These chatbots can qualify leads by asking pre-programmed questions, allowing businesses to capture information and engage prospects 24/7. A retail company using Drift reported a 40% increase in qualified leads due to the AI’s ability to engage customers outside of regular business hours. Consider LinkedIn Sales Navigator which leveraging AI, helps businesses find leads by analyzing user profiles and activities on LinkedIn. It suggests potential leads based on a company’s customer preferences and search history. A financial services company credited Sales Navigator with a 20% increase in new client acquisitions.

Moreover, MarketMuse uses AI to analyze content and suggest topics that attract and engage the target audience. A content marketing agency using MarketMuse experienced a 50% increase in web traffic, leading to a higher volume of inbound leads. Then, we have IBM Watson’s Personality Insights: This is a tool that analyzes communication styles and personality traits. A business consultancy used this tool to tailor its communication strategy to each lead’s personality, resulting in a 35% higher engagement rate.

Continue reading “The AI Revolution In Lead Generation: Navigating New Business Frontiers” | >

A study led by the University of Oxford has used the power of machine learning to overcome a key challenge affecting quantum devices. For the first time, the findings reveal a way to close the ‘reality gap’: the difference between predicted and observed behavior from quantum devices. The results have been published in Physical Review X.

Quantum computing could supercharge a wealth of applications, from climate modeling and financial forecasting, to drug discovery and artificial intelligence. But this will require effective ways to scale and combine individual quantum devices (also called qubits). A major barrier against this is inherent variability: where even apparently identical units exhibit different behaviors.

The cause of variability in quantum devices.

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Winter in the northern hemisphere is always a brutal reminder for the shipping industry that routing vessels efficiently is a big challenge. Winter storms bring low visibility conditions, freezing spray, and sea ice, all of which can lead to catastrophic results if not appropriately navigated, including lost cargo, damaged hulls and even potentially toppling a ship in the most extreme weather. But this January adds additional pressures to the sector with new and enacted regulations around greenhouse emissions and carbon usages. The beneficial news is that in both scenarios, weather intelligence can help those navigating the open seas better plan and safely and efficiently navigate these waters.

While most of us know that weather impacts nearly every aspect of shipping, we likely think of it in terms of safety of people and cargo. According to The Swedish Club 2020 loss prevention report, heavy weather is cited in half of all claims and contributes to 80% of the financial losses. Weather optimized routing uses real-time weather forecasts, oceanic data, and the vessel’s current position to keep captains at sea and voyage managers on land about changing conditions. If there is hazardous weather, most voyage routing algorithms can make numerous calculations in real time and provide one or more alternatives for a ship operator to optimize a route. While ultimately this may not be the most efficient route, it will likely be the safest route for current conditions.

Weather intelligence is also critical in evaluating, and potentially adjusting, greenhouse gas emissions based on vessel performance and fuel usage. The Carbon Intensity Indicator (CII) introduced in 2023 is a rating framework that evaluates how efficiently a ship transports goods or passengers from a carbon emissions standpoint. This is the first year that ships will be assigned a rating. The data from the previous year is used in an efficiency conversion ratio. Each ship is assigned an individual CII rating from A to E, with A being the best possible rank.

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