Galleon Microbiome Analysis a comprehensive broiler microbiome health assessment toolThere is an interdependency between the condition of the gut microbiome and the flock's health. Therefore, understanding the gut microbiome allows producers to optimize animal health and performance. Cargill's patent-pending Galleontoolenables broiler producers to decide how changes such as in raw materials, diet, additives, vaccine programs, and farm management practices influence the microbiome of their flock.

Using a simple swab from a live bird, Cargill scientists analyze a customer's flock health using Galleon's robust database of poultry microbiome, developed over a decade using a global data set and nearly 100 trial studies. The analysis is further augmented using statistical analysis, machine learning and artificial intelligence capabilities to provide producers with a comprehensive report and recommended interventions to address issues. In addition, results are unbiased towards a specific product.

Galleon can help producers:

"An animal's gut microbiome influences its health in so many ways," said Cargill's principal microbiome researcher, Dr. Briana Kozlowicz. "We've accumulated an industry-leading volume of microbiome data that we can now tap into to provide actionable insights to our customers to improve the performance of their flocks."

Birdoo for real-time, hands-free measurement of broiler weight performance through advanced imaging and predictive analysisFeed is the highest cost input for poultry producers and the primary contributor to their birds' health. At the same time, obtaining accurate animal weight is a time-consuming and labor-intensive flock management effort.

To help producers better track broiler performance,Cargill has teamed up with digital technology enablementfirm, Knex, to develop 'Birdoo," a first-of-its-kind technology thatleverages proprietary computer visioning and artificial intelligence that combines hands-free, real-time flock insights with predictive modeling data. This helps producers make informed decisions quicker while supporting their bottom lines through better animal health and well-being, increased uniformity and improved performance of their flocks.

Birdoo will help Cargill producers:

"We talk with our customers every day, listen to what they need, and are committed to delivering innovative solutions, like Galleon and Birdoo, to help their businesses thrive," says Adriano Marcon, President of Cargill's animal nutrition business."We're combining our deep animal nutrition expertise with leading-edge technologies to deliver actionable insights that address their unique animal health and production challenges."

Cargill innovation demos at the International Production & Processing Expo (IPPE)To see in-person demonstrations of Galleon and Birdoo at the upcoming IPPE, visit the Cargill's booth #B8159 and attend the "Tech Talks" featuring Galleon on January 25 and Birdoo on January 26.

Additional broiler performance solutions for poultry producers to consider

To learn more about any of Cargill's solutions for poultry production, reach out to your local Cargill representative.

About CargillCargill's 155,000 employees across 70 countries work relentlessly to achieve our purpose of nourishing the world in a safe, responsible and sustainable way. Every day, we connect farmers with markets, customers with ingredients, and people and animals with the food they need to thrive.

We combine 155 years of experience with new technologies and insights to serve as a trusted partner for food, agriculture, financial and industrial customers in more than 125 countries. Side-by-side, we are building a stronger, sustainable future for agriculture. For more information, visitCargill.comand ourNews Center.

SOURCE Cargill, Inc.

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Cargill expands portfolio of artificial intelligence-powered innovations to give poultry producers actionable insights - PRNewswire

The megacap tech stocks have had a solid if not spectacular 2021. Heading into the final days of the year, Google parent Alphabet GOOG, +0.04% has soared 67%, while Amazon.com AMZN, -0.86% has only gained 5%, as founder Jeff Bezos exited for quite literally greater horizons. The NYSE FANG+ NYFANG, -0.81% index, which includes the five core FAANG stocks (Facebook, Amazon, Apple , Netflix and Google) plus a handful more including Nvidia and Tesla, has gained 19% underperforming both the S&P 500 SPX, +0.14% and Nasdaq Composite COMP, -0.10%.

The artificial-intelligence Powered Equity ETF AIEQ, +0.35% seems to have caught on. Its an exchange-traded fund that uses IBM Watson to pick stocks, and now it doesnt have any of the megacap tech giants in its top 10 holdings. Jessica Rabe, co-founder of DataTrek Research, points out that as recently as September, Apple AAPL, +0.05%, Microsoft MSFT, +0.21%, Amazon and Alphabet were its top four positions, making up nearly a quarter of the exchange-traded fund. Even in November, Microsoft, Alphabet and Amazon accounted for about 15% of the fund. Now, only Apple of the FAANG stocks is in the portfolio.

What is the fund doing now? It still has a smattering of tech stocks in its top 10, led by microchip maker Advanced Micro Devices AMD, -3.19%, but also investments including diabetes monitoring system maker Dexcom DXCM, +1.11% and electrical-system maker Eaton ETN, +0.30%. Theres also a bit of a cybersecurity theme with both Palo Alto Networks PANW, -0.20% and Fortinet FTNT, +0.20% in its top 10.

AIEQ has been diversifying its holdings in a host of industries and putting most of its capital to work. Thats in contrast to this past September, for example, when it placed more concentrated investments in well-known companies amid that choppy month for U.S. equities. This latest approach reflects the current positive investment environment with the S&P near record highs, says Rabe.

Tesla TSLA, -0.21% CEO Elon Musk sold another $1 billion of stock in the electric-vehicle maker, according to Securities and Exchange Commission filings, to pay the taxes for the exercise of a 1.55 million share option. That should wrap up his preplanned stock sales for this year.

Apple AAPL, +0.05% is reportedly paying up to $180,000 to prevent employees from moving to tech rivals including Meta Platforms FB, -0.95%, according to Bloomberg News.

The trade deficit in goods ballooned by 17.5% in November, the Commerce Department reported. Retail inventories rose by 2%, and wholesale inventories rose by 1.2%, the same report said. Pending home sales data is due shortly after the open.

The World Health Organization reported that the number of COVID-19 cases worldwide climbed 11% last week, with Europe having the highest infection rate of any region.

Stock futures ES00, -0.05% NQ00, -0.07% were flattish after a pause in the rally on Tuesday.

The yield on the 10-year Treasury TMUBMUSD10Y, 1.545% edged up to 1.52%. One big move was in European natural-gas contracts, with the lead U.K. contract GWM00, +1.21% tumbling 5% as a combination of warmer weather, U.S. supplies and Norwegian output moved prices off recent highs.

Here are the top tickers on MarketWatch, as of 6 a.m. Eastern.

This tax maneuver from the 1990s is now helping Silicon Valley tycoons save tens of millions of dollars.

These brothers have re-gifted the same candy since 1987.

Need to Know starts early and is updated until the opening bell, but sign up here to get it delivered once to your email box. The emailed version will be sent out at about 7:30 a.m. Eastern.

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This fund driven by artificial intelligence is ditching big tech. Here's what it's doing instead. - MarketWatch

2021 was the year in which the wonders of artificial intelligence stopped being a story. Which is not to say that IEEE Spectrum didn't cover AIwe covered the heck out of it. But we all know that deep learning can do wondrous things and that it's being rapidly incorporated into many industries; that's yesterday's news. Many of this year's top articles grappled with the limits of deep learning (today's dominant strand of AI) and spotlighted researchers seeking new paths.

Here are the 10 most popular AI articles that Spectrum published in 2021, ranked by the amount of time people spent reading them. Several came from Spectrum's October 2021 special issue on AI, The Great AI Reckoning.

1. Deep Learning's Diminishing Returns: MIT's Neil Thompson and several of his collaborators captured the top spot with a thoughtful feature article about the computational and energy costs of training deep learning systems. They analyzed the improvements of image classifiers and found that "to halve the error rate, you can expect to need more than 500 times the computational resources." They wrote: "Faced with skyrocketing costs, researchers will either have to come up with more efficient ways to solve these problems, or they will abandon working on these problems and progress will languish." Their article isn't a total downer, though. They ended with some promising ideas for the way forward.

2. 15 Graphs You Need to See to Understand AI in 2021: Every year, The AI Index drops a massive load of data into the conversation about AI. In 2021, the Index's diligent curators presented a global perspective on academia and industry, taking care to highlight issues with diversity in the AI workforce and ethical challenges of AI applications. I, your humble AI editor, then curated that massive amount of curated data, boiling 222 pages of report down into 15 graphs covering jobs, investments, and more. You're welcome.

3. How DeepMind Is Reinventing the Robot: DeepMind, the London-based Alphabet subsidiary, has been behind some of the most impressive feats of AI in recent years, including breakthrough work on protein folding and the AlphaGo system that beat a grandmaster at the ancient game of Go. So when DeepMind's head of robotics Raia Hadsell says she's tackling the long-standing AI problem of catastrophic forgetting in an attempt to build multi-talented and adaptable robots, people pay attention.

4. The Turbulent Past and Uncertain Future of Artificial Intelligence: This feature article served as the introduction to Spectrum's special report on AI, telling the story of the field from 1956 to present day while also cueing up the other articles in the special issue. If you want to understand how we got here, this is the article for you. It pays special attention to past feuds between the symbolists who bet on expert systems and the connectionists who invented neural networks, and looks forward to the possibilities of hybrid neuro-symbolic systems.

5. Andrew Ng X-Rays the AI Hype: This short article relayed an anecdote from a Zoom Q&A session with AI pioneer Andrew Ng, who was deeply involved in early AI efforts at Google Brain and Baidu and now leads a company called Landing AI. Ng spoke about an AI system developed at Stanford University that could spot pneumonia in chest x-rays, even outperforming radiologists. But there was a twist to the story.

6. OpenAI's GPT-3 Speaks! (Kindly Disregard Toxic Language): When the San Francisco-based AI lab OpenAI unveiled the language-generating system GPT-3 in 2020, the first reaction of the AI community was awe. GPT-3 could generate fluid and coherent text on any topic and in any style when given the smallest of prompts. But it has a dark side. Trained on text from the internet, it learned the human biases that are all too prevalent in certain portions of the online world, and can therefore has an awful habit of unexpectedly spewing out toxic language. Your humble AI editor (again, that's me) got very interested in the companies that are rushing to integrate GPT-3 into their products, hoping to use it for such applications as customer support, online tutoring, mental health counseling, and more. I wanted to know: If you're going to employ an AI troll, how do you prevent it from insulting and alienating your customers?

7. Fast, Efficient Neural Networks Copy Dragonfly Brains: What do dragonfly brains have to do with missile defense? Ask Frances Chance of Sandia National Laboratories, who studies how dragonflies efficiently use their roughly 1 million neurons to hunt and capture aerial prey with extraordinary precision. Her work is an interesting contrast to research labs building neural networks of ever-increasing size and complexity (recall #1 on this list). She writes: "By harnessing the speed, simplicity, and efficiency of the dragonfly nervous system, we aim to design computers that perform these functions faster and at a fraction of the power that conventional systems consume."

8. Deep Learning Isn't Deep Enough Unless It Copies From the Brain: In a former life, Jeff Hawkins invented the PalmPilot and ushered in the smartphone era. These days, at the machine intelligence company Numenta, he's investigating the basis of intelligence in the human brain and hoping to usher in a new era of artificial general intelligence. This Q&A with Hawkins covers some of his most controversial ideas, including his conviction that superintelligent AI doesn't pose an existential threat to humanity and his contention that consciousness isn't really such a hard problem.

9. The Algorithms That Make Instacart Roll: It's always fun for Spectrum readers to get an insider's look at the tech companies that enable our lives. Engineers Sharath Rao and Lily Zhang of Instacart, the grocery shopping and delivery company, explain that the company's AI infrastructure has to predict the availability of "the products in nearly 40,000 grocery storesbillions of different data points," while also suggesting replacements, predicting how many shoppers will be available to work, and efficiently grouping orders and delivery routes.

10. 7 Revealing Ways AIs Fail: Everyone loves a list, right? After all, here we are together at item #10 on this list. Spectrum contributor Charles Choi pulled together this entertaining list of failures and explained what they reveal about the weaknesses of today's AI. The cartoons of robots getting themselves into trouble are a nice bonus.

So there you have it. Keep reading IEEE Spectrum to see what happens next. Will 2022 be the year in which researchers figure out solutions to some of the knotty problems we covered in the year that's now ending? Will they solve algorithmic bias, put an end to catastrophic forgetting, and find ways to improve performance without busting the planet's energy budget? Probably not all at once... but let's find out together.

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2021's Top Stories About AI - IEEE Spectrum

12/29/2021 at 5:52 PM CET

Carles Planas Bou

Spain will have an agency dedicated to monitoring and minimizing significant risks to the safety and health of people, as well as their fundamental rights & rdquor; that may cause the use of Artificial intelligence (IA). This is stated in the Law of General State Budgets (PGE) approved on Tuesday and published this Wednesday in the BOE.

The pact for the 2022 budgets establishes that the Spanish Agency for the Supervision of Artificial Intelligence will have an endowment of 5 million euros to investigate the danger that may arise from algorithms. And it is that, whether they are private companies or public administrations, more and more people use these to automate all kinds of processes, from personalizing content on the Internet to granting bank loans or state aid. Although it operates under the false guise of mathematical neutrality, this technology is imperfect and can amplify potential racist, gender or class discrimination against part of society.

Aware of this, the Spanish agency will be in charge of auditing the algorithmic systems used in the territory in a transparent, objective and impartial way & rdquor; and will provide advice to the unions so that the platforms comply with the regulation of the algorithms established by the Rider Law. It will also have the ability to apply sanctions, although the law does not give more details about it.

The creation of this AI oversight body comes after a proposal launched in November by More CountryEquo and agreed with the two Government partners, PSOE and we can. Its approval in the budgets for 2022 comes as the European Union (EU) prepares a community regulation to limit the impact of these algorithmic systems that will include a ban on mass surveillance systems.

The governments gesture also responds to growing international pressure to regulate the use of AI and its potential social dangers. The United Nations, for example, has warned of these risks and has asked both states and companies to dramatically increase transparency & rdquor; about the algorithmic systems that are used on a day-to-day basis. On December 9, an international group of experts asked in the journal Science that, given the growing distrust with this technology, a global network of ethical hackers was put in place to uncover their vulnerabilities and fix them before they explode against society.

However, all of that is still on paper. In order for the agency to begin to move forward, it will first have to agree on a law that establishes its creation in detail, as well as the development of an initial action plan. All of this means that this promise to curb the risks of AI can take months to take shape.

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Spain will create an agency to monitor the dangers of Artificial Intelligence - Central Valley Business Journal

The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD program) was created to combat harmful biases in how artificial intelligence and machine learning is used.

KERA's Justin Martin talked with UNTHSC's Dr. Jamboor Vishwanatha, about what this means for North Texas.

Interview Highlights:

On the AIM-AHEAD program:

AIM-AHEAD is a consortium to promote artificial intelligence and machine learning to achieve health equity and also diversify the research workforce that is involved in the AI (artificial intelligence) and ML (machine learning) work. So it basically attacks two different issues.

One is the lack of diversity in the data that is currently used in the AI/ML field. And secondly, who is actually doing the work.

So this is actually a very, very significant program, and I think you mentioned about $100 million. It is one large investment that NIH has made in terms of diversity efforts.

On how artificial intelligence and machine learning affects health care:

Artificial intelligence and machine learning are in every walk of life. Basically, when you wear a smartwatch, you're pretty much collecting data. And then there are a lot of decisions that are made.

Artificial intelligence is used in the hospitals, in the clinics. It is being used in all walks of life. What is going to happen is some of the clinical decisions will all be based on machine learning, on artificial intelligence.

So it is quite important at this point for us to make sure that people are not left behind, that their data is used in developing the algorithms in making sure that any outcome is representative of all the population.

On how AI/ML lacks diversity:

There are two issues. One is the gender. It turns out that most of the people who are working in this area are predominantly male. I mean, so you don't really have gender equity.

Secondly, racial ethnic groups are not highly represented in the workforce that is currently doing artificial intelligence machine learning. And therefore, this is really critical that we involve all of the groups, all of the racial ethnic groups in the future workforce.

Dr. Jamboor Vishwanatha is regents professor and vice president of diversity and international programs at the University of North Texas Science Center in Fort Worth.

Interview highlights were lightly edited for clarity.

Got a tip? Email Justin Martin atJmartin@kera.org. You can follow Justin on Twitter @MisterJMart.

KERA News is made possible through the generosity of our members. If you find this reporting valuable, considermaking a tax-deductible gifttoday.

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$100 million awarded to UNT's Health Science Center to diversify field of AI - KERA News

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United States Artificial Intelligence (AI) In Drug Discovery Market Research Report 2021: Prospects, Trends Analysis, Market Size and Forecasts to...

In the first of his four (stunning) Reith lectures on living with artificial intelligence, Prof Stuart Russell, of the University of California at Berkeley, began with an excerpt from a paper written by Alan Turing in 1950. Its title was Computing Machinery and Intelligence and in it Turing introduced many of the core ideas of what became the academic discipline of artificial intelligence (AI), including the sensation du jour of our own time, so-called machine learning.

From this amazing text, Russell pulled one dramatic quote: Once the machine thinking method had started, it would not take long to outstrip our feeble powers. At some stage therefore we should have to expect the machines to take control. This thought was more forcefully articulated by IJ Good, one of Turings colleagues at Bletchley Park: The first ultra-intelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

Russell was an inspired choice to lecture on AI, because he is simultaneously a world leader in the field (co-author, with Peter Norvig, of its canonical textbook, Artificial Intelligence: A Modern Approach, for example) and someone who believes that the current approach to building intelligent machines is profoundly dangerous. This is because he regards the fields prevailing concept of intelligence the extent that actions can be expected to achieve given objectives as fatally flawed.

AI researchers build machines, give them certain specific objectives and judge them to be more or less intelligent by their success in achieving those objectives. This is probably OK in the laboratory. But, says Russell, when we start moving out of the lab and into the real world, we find that we are unable to specify these objectives completely and correctly. In fact, defining the other objectives of self-driving cars, such as how to balance speed, passenger safety, sheep safety, legality, comfort, politeness, has turned out to be extraordinarily difficult.

Thats putting it politely, but it doesnt seem to bother the giant tech corporations that are driving the development of increasingly capable, remorseless, single-minded machines and their ubiquitous installation at critical points in human society.

This is the dystopian nightmare that Russell fears if his discipline continues on its current path and succeeds in creating super-intelligent machines. Its the scenario implicit in the philosopher Nick Bostroms paperclip apocalypse thought-experiment and entertainingly simulated in the Universal Paperclips computer game. It is also, of course, heartily derided as implausible and alarmist by both the tech industry and AI researchers. One expert in the field famously joked that he worried about super-intelligent machines in the same way that he fretted about overpopulation on Mars.

But for anyone who thinks that living in a world dominated by super-intelligent machines is a not in my lifetime prospect, heres a salutary thought: we already live in such a world! The AIs in question are called corporations. They are definitely super-intelligent, in that the collective IQ of the humans they employ dwarfs that of ordinary people and, indeed, often of governments. They have immense wealth and resources. Their lifespans greatly exceed that of mere humans. And they exist to achieve one overriding objective: to increase and thereby maximise shareholder value. In order to achieve that they will relentlessly do whatever it takes, regardless of ethical considerations, collateral damage to society, democracy or the planet.

One such super-intelligent machine is called Facebook. And here to illustrate that last point is an unambiguous statement of its overriding objective written by one of its most senior executives, Andrew Bosworth, on 18 June 2016: We connect people. Period. Thats why all the work we do in growth is justified. All the questionable contact importing practices. All the subtle language that helps people stay searchable by friends. All of the work we have to do to bring more communication in. The work we will likely have to do in China some day. All of it.

As William Gibson famously observed, the futures already here its just not evenly distributed.

Pick a sideThere Is no Them is an entertaining online rant by Antonio Garca Martnez against the othering of west coast tech billionaires by US east coast elites.

Vote of confidence?Can Big Tech Serve Democracy? is a terrific review essay in the Boston Review by Henry Farrell and Glen Weyl about technology and the fate of democracy.

Following the rulesWhat Parking Tickets Teach Us About Corruption is a lovely post by Tim Harford on his blog.

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Worried about super-intelligent machines? They are already here - The Guardian

The global market for artificial intelligence (AI) in computer vision is projected to grow at a compound annual growth rate (CAGR) of 39.4% during the projected period from 2022 to 2030, reaching US $ 20.76 billion in 2030. The global market for artificial intelligence (AI) computer vision in 2020 is US $ 9.16 billion.

Computer vision systems determine meaningful information from visual inputs such as digital images and videos, and take actions and recommendations based on that information. Computer vision is very similar to human vision, but there are some advantages to human vision. Through lifelong experience, human vision distinguishes objects, detects movements, and images. You can learn to determine if is correct, etc. Computer vision can work as well.

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Factors that influence market growth

Market driving force-Expanding the use of computer vision systems in automotive applications , The spread of emotional AI, high quality inspection and automation are driving the global market.

Market threats increasing interest in safety and security is the main cause of slowing global market growth.

Market Growth -Many automakers and IT giants are developing autonomous vehicles, which is driving the growth of the global artificial intelligence (AI) market for computer vision.

Market Opportunities Artificial Intelligence Government initiative to drive the development of (AI) related technologies provides an opportunity for the entire computer vision artificial intelligence (AI) market.

COVID-19 Impact Analysis

COVID-19 is a global and community Many industrial sectors and companies struggled to secure resources during the pandemic (COVID-19). As a result of the pandemic, artificial intelligence This is because the demand for (AI) technology is increasing and many high-tech companies are developing solutions to prevent, control and mitigate viruses. As a result, the computer vision artificial intelligence (AI) market is COVID-19. Achieved positive growth during.

Further Report Highlights

In the Type segment, the hardware segment dominates the global market for artificial intelligence (AI) in computer vision in 2020. In the

regional segment, the Asia-Pacific region will dominate the global market in 2020 . It is the result of increased investment by Chinese companies to expand the scope of computer vision technology.

North America is expected to see significant market growth. Government efforts to promote the introduction of computer vision in the region , have contributed to this growth.

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List of major companies in the global market profiling of artificial intelligence (AI) in computer vision NVIDIA Corporation Intel Corporation Microsoft Corporation IBM Corporation Qualcomm Amazon Web Services, Incorporated Google, LLC Meta Platforms, Incorporated Xilinx, Incorporated BASLER AG Other Prominent Players

Segmental Analysis

The global market for artificial intelligence (AI) in computer vision focuses on components, features, applications, end-uses, and regions.

Component-based segmentation hardware Processor (CPU) Central processing unit (GPU) Graphics processing unit (ASIC) Integrated circuit for specific applications (FPGA) Field Programmable Gate Array memory storage software

Function-based segmentation training interference

Application-based segmentation Industrial Non-industrial

End-use based segmentation Automotive related Consumer electronics Healthcare Agriculture Transportation / Logistics retail

Security and surveillance Manufacturing industry others

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By region,

North America America Canada Mexico

Europewestern Europe England Germany France Italy Spain Other Western European countries

Eastern Europe Poland Russia Other Eastern European countries

Asia-Pacific China India Japan Australia / New Zealand Association of Southeast Asian Nations Other Asia Pacific regions

Middle East / Africa (MEA)United Arab Emirates (UAE) Saudi Arabia South Africa Other Middle East / Africa regions

South America Brazil Argentina Other South American regions

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The Global Market for Artificial Intelligence (AI) in Computer Vision is projected to grow at a compound annual growth rate (CAGR) of 39.4% during the...

Dublin, Dec. 29, 2021 (GLOBE NEWSWIRE) -- The "AI and Advance Machine Learning in BFSI Market By Component, Deployment Model, Enterprise Size and Application: Global Opportunity Analysis and Industry Forecast, 2021-2030" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) in finance is transforming the BFSI industry, as AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. In addition, advanced machine learning technology is being used to help organizations to improve customer experience and to enhance their market share.

Furthermore, it provides various solutions to the baking sector to replace routine manual work with automation and to increase productivity. In addition, AI and advanced machine learning help in reducing credit default frauds by monitoring transactions to detect suspicious transactions with compliance concerns.

Improvement in data collection technology among the banks and financial institutions positively impacts the growth of the market. In addition, an increase in investment by BFSI companies in AI and machine learning and a rise in customer preferences for personalized financial services boost the growth of the market across the globe.

However, factors such as the higher deployment cost of AI and advance machine learning and lack of skilled labor are limiting the growth of the market. On the contrary, the surge in the adoption of modern applications in the BFSI sector is expected to offer remunerative opportunities for the expansion of the market during the forecast period.

The global AI and advance machine learning in BFSI market is segmented into component, deployment model, enterprise size, application and region. Depending on the component, the market is segregated into solutions and services.

On the basis of deployment model, it is categorized into on-premise and cloud. Depending on enterprise size, it is fragmented into large enterprises and SMEs.

Based on application, the market is divided into fraud & risk management, customer segmentation, sales & marketing, digital assistance and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.

The key players profiled in the AI and advance machine learning in BFSI market analysis are:

These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

Key Market Segments

By Component

By Deployment Model

By Enterprise Size

By Application

By Region

Key Topics Covered:

CHAPTER 1: INTRODUCTION1.1. Report description1.2. Key benefits for stakeholders1.3. Key market segments1.4. Research methodology

CHAPTER 2: EXECUTIVE SUMMARY2.1. Key findings2.2. CXO perspective

CHAPTER 3: MARKET OVERVIEW3.1. Market definition and scope3.2. Key forces shaping global artificial intelligence and advanced machine learning in BFSI market3.3. Case studies3.3.1. CargoSmart adopted Tibco advance analytics solution for improving its decision-making capability by using real-time analysis3.3.2. Honeywell International Inc. adopted data and business analytics platform of Expedien Inc. to increase productivity, lower risk costs, accelerate growth, and lower risk of organizations3.4. Market dynamics3.4.1. Drivers3.4.1.1. Increase in investment by BFSI companies in AI and machine learning3.4.1.2. Increasing preferences for personalized financial services3.4.1.3. Increase in collaboration between financial institutes and AI & machine learning solution company3.4.2. Restraint3.4.2.1. Higher deployment cost of AI and advanced machine learning3.4.2.2. Lack of skilled labor3.4.3. Opportunity3.4.3.1. Increase in government initiatives and growth in investments to leverage AI technology3.5. Market evolution/industry roadmap3.6. Impact of government regulations on the global artificial intelligence and advanced machine learning in BFSI market3.7. COVID-19 impact analysis on AI and Advanced Machine Learning in BFSI market3.7.1. Impact on market size3.7.2. Consumer trends, preferences, and budget impact3.7.3. Economic impact3.7.4. Strategies to tackle the negative impact3.7.5. Opportunity window3.8. Key future initiatives3.8.1. Product launches

CHAPTER 4: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY COMPONENT4.1. Overview4.2. Solution4.3. Service

CHAPTER 5: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY DEPLOYMENT MODEL5.1. Overview5.2. On-premise5.3. Cloud-based

CHAPTER 6: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY ENTERPRISE SIZE6.1. Overview6.2. Large enterprise6.3. SMEs

CHAPTER 7: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY APPLICATION7.1. Overview7.2. Fraud & Risk Management7.3. Customer Segmentation7.4. Sales & Marketing7.5. Digital Assistance7.6. Others

CHAPTER 8: GOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY REGION8.1. Overview8.2. North America8.3. Europe8.4. Asia-Pacific8.5. LAMEA

CHAPTER 9: COMPETITIVE LANDSCAPE9.1. Key players positioning analysis, 20209.2. Competitive dashboard9.3. Top winning strategies

CHAPTER 10: COMPANY PROFILE10.1. Amazon Web Services, Inc.10.2. BigML, Inc.10.3. Cisco System Inc.10.4. FAIR ISAAC CORPORATION10.5. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP10.6. INTERNATIONAL BUSINESS MACHINES CORPORATION10.8. MICROSOFT CORPORATION10.9. RapidMiner, Inc.10.10. SAP SE10.11. SAS INSTITUTE INC.

For more information about this report visit https://www.researchandmarkets.com/r/nzvud5

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Global AI and Advance Machine Learning in BFSI Market Report (2021 to 2030) - GlobeNewswire

Almost all workplaces and college campuses offer a vending machine filled with snack items such as chips, pretzels, cookies, and candy. While shelf stable, these sugary and salty snack items are less nutritious and not nearly as tasty as fresher options like fresh sandwiches or salads. And yet these long-lasting, less healthy items persist as a mainstay in the office vending machine because fresh items expire three to four days after they are prepared, therefore increasing the risk of these products ending up in a landfill.

Foodoo.ai wants to change all that by giving offices the ability to offer fresh food without all the food waste. The Czech Republic-based startups mission is to provide food delivery to offices with what it calls zero waste nutrition. The company works with certified commercial kitchens to prepare fresh, healthy food options, delivers them to workplaces, and then uses its proprietary software and hardware to ensure little to no food is wasted.

Once Foodoo.ais kitchen partners finish prepping individually packaged fresh food dishes like sandwiches, veggies and dips, soups, and salads, the company then attaches an RFID (radio-frequency identification) tag to the outside of the packaging. This tag contains important information like the dishs name, the ingredients, and the expiration date; all of this information is sent to the companys data center.

Foodoo.ai has developed a proprietary hardware kit that can be installed into any existing refrigerator, mini-fridge, vending machine, or other food storage unit. Without the need to develop its own smart fridge or vending machine, Foodoo.ai can scale faster while also giving customers a lower-cost option that doesnt involve swapping out their fridge.

The hardware system consists of scanners that keep track of product stock and the expiration dates. The data that is collected from the hardware is sent to Foodoo.ais cloud-based software, which uses machine learning to provide insight into the shelf life of products, how much product is left at the end of the day, anticipated consumption rate, user behavior, and what products are in high demand.

The grab-and-go food market has become increasingly more popular with millennials. Prepackaged, short shelf-life food items were originally considered low-quality food, often associated with gas stations or corner markets. Now, prepackaged grab-and-go food is depended on by office workers, college students, travelers, and those working or visiting hospitals.

Most people have particular food preferences, dietary restrictions, and allergies, and unfortunately, grab-and-go food cannot be customized to accommodate this. This might lead to customers picking at certain parts of the dish, and throwing the rest away. If customer demand and traffic are not accounted for, this can quickly lead to packaged fresh foods expiring and being thrown out.

According to Foodoo.ai, about 17 percent of the food it delivers to workplaces is wasted. As the companys artificial intelligence gleans more information, Foodoo.ai believes its system can gain even more insight into food preferences and consumer behavior and help to reduce the amount of grab-and-go food wasted.

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