Category Archives: Artificial Intelligence

Artificial intelligence: its one of those mind-bending subjects that provokes fascination yet is misunderstood by almost everyone (myself included). If you bring it up in the pub to sound brainy, the conversation might only go as far as Alan Turing, Deep Blue versus Garry Kasparov and perhaps the new series of BBC drama The Capture, in which foreign spies seem to use AI to manipulate CCTV and live broadcasts.

But AI has long moved beyond the realms of gripping fiction and headline-grabbing experiments into the real world. For instance, DeepMind, a British subsidiary of Alphabet, recently released almost the entire make-up of the protein universe, mapped for the first time by its AI programme AlphaFold. This data is already being used to advance our understanding of life-threatening illnesses and help protect honeybees.

Amid all the scaremongering about AI, perhaps we should be more aware of these positive developments, particularly if were investors. Funding technology that is tangibly solving the worlds trickiest problems surely thats responsible investing at its finest?

Besides, AI applications that drive efficiency, reduce costs, serve vital needs, and cement a companys competitive advantage should generate significant value. Thats not to be sniffed at in this current investing climate.

The Sanlam Artificial Intelligence Fund has specifically hit on AI as a way to back winners in the stock market. Its managers Chris Ford and Tim Day marked the funds fifth anniversary recently by sharing some intriguing insights on how AI has improved in recent times.

Things that people called pipe dreams when we launched the fund in 2017 are now happening, says Chris Ford. What was impossible is now possible.

Examples include Alibabas equivalent scoring better than humans in Stamford Universitys reading and comprehension test, and the Canadian platform Blue Dot spotting the spread of Covid-19 nine days before the World Health Organisation put the alert out.

How has this translated into hard financial returns? The 715.5m fund has had an annualised return of 18.2 per cent since it was established, which is respectable, and its top performers have included household names like Tesla, Netflix, Microsoft and Ocado, alongside lesser-known AI innovators like NVIDIA, Zendesk, and Appen.

The managers even have their own AI application (Orbit) to help them stay across things. Its ongoing charges figure is 0.8 per cent not dirt cheap, but not bad for an active fund.

Do actively managed thematic funds like this have the edge, not just over their cheaper rivals but within the broader investment universe?

Sanlams fund, a traditional open-ended unit trust, has comfortably beaten the MSCI World index, which has had an annualised return of 8.41 per cent over the past five years. By contrast, only 39 per cent of thematic exchange-traded funds (ETFs) have survived and outperformed the index over that same period, according to data from Morningstar.

But the funds performance has markedly dipped in recent times, as the global rout in stock markets hits technology shares particularly hard. This is the downside of thematic investing: clever use of AI may not be the main reason why a company flourishes, or why investors feel well-disposed towards it.

The likes of Tesla, Ocado and Netflix can end up in choppy waters when investing conditions change, and it becomes clear that these firms have been overvalued by the market for what they do. Thematic investing can also lead to a good deal of overlap in your portfolio, with the same names cropping up again and again. This can undermine diversification and add to your risks.

Also, whilst many of the Sanlam holdings are doing great work (like Alphabets DeepMind), there is a good reason why its not classed as socially responsible. EthicsGrade is a ratings agency specialising in understanding the risks stemming from digitalisation, particularly AI, and it has awarded a top A rating to only a handful of firms, including Microsoft and Deutsche Telekom.

Fewer than a third of the 302 major companies it graded have a score of C or above. Too many were unrated altogether because they either ignored or provided little to no details on governance and ethics policies relating to AI.

Experts mostly agree that the more sinister AI possibilities as depicted in The Capture still look far-fetched. But its worrying that many of the major companies we buy from and invest in are being cavalier regarding privacy breaches, data misuse, racial and gender discrimination, the creation of harmful weapons and other potential dangers of AI.

AI is not going anywhere (particularly since the Chinese government is investing heavily in AI research and development). If it proves as beneficial for its first movers as many believe, AI could become the first big success story of so-called thematic investing, previously dismissed as no more than a trendy gimmick that promises more than it can deliver.

The big question is whether this technology will be treated with enough care by its creators and investors. If not, disappointing returns will be the least of our problems.

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Investing in artificial intelligence: The pros and cons - iNews

There are many examples that could be given to demonstrate the benefits of technology and Artificial Intelligence in supporting the rehabilitation of people who have health issues.

To start with, there is the project being carried out in the intensive care unit (ICU) of the Hospital del Mar in Barcelona, where people admitted to hospital can start their recovery by taking a ride on a bicycle without actually leaving their bed, in a process that combines physical exercise and virtual reality.

This idea consists of an ergometric bicycle that allows bedridden patients to do physical activity similar to cycling, combined with virtual reality to start their rehabilitation process in an early and enjoyable manner.

The system allows the patient to pedal even when lying in bed and adapts to their physical capacity, even having the capacity to have them move their legs passively and thus mobilising the muscles when the patient does not yet have the capacity to do so themselves.

In addition, the bicycle is combined with a 360-degree reality glasses system, which allows patients to feel as if they were actually cycling through different areas of the city of Barcelona.

To do this, they work with the Barcelona-based company Realitytelling, which produces the video capsules that patients watch while they exercise, making it necessary to synchronise the video with the rhythm of pedalling to make the experience more satisfactory.

New technologies are also helping to alleviate as far as possible the effects of degenerative diseases such as Alzheimers, Parkinsons or others, such as Amyotrophic Lateral Sclerosis (ALS).

In the case of Parkinsons disease, work is under way on a new brain stimulation treatment that could improve quality of life and help control symptoms in people with advanced cases of Parkinsons.

Research, conducted by the University of Florida in conjunction with fourteen medical centres, tested the effectiveness and safety of a device that helps improve symptoms through deep brain stimulation (DBS).

The device aims to reduce tremors, improve the slowness of movement, reduce motor disability and involuntary movements related to the disease and the drugs used to treat it.

To do this, the system works through small electrodes implanted in the brain connected to the device which is programmed to emit a mild electrical current.

Artificial Intelligence takes centre stage in a European project involving researchers from the University of Castilla-La Mancha (UCLM), through the Faculty of Physiotherapy and Nursing, for use in the functional diagnosis of children with hemiparesis (paralysis on one side of the body) and also the construction of home rehabilitation systems (telerehabilitation).

This is the European project AInCP (Artificial Intelligence in Cerebral Palsy), led by the Italian University of Pisa, which will clinically validate new Artificial Intelligence algorithms to develop clinical tools that support evidence-based decisions in the functional diagnosis of children with hemiparesis and create rehabilitation systems that they can access at home.

Several Spanish universities are collaborating on a project in which they seek to improve the behaviour and response of children with autism spectrum disorders (ASD) by means of social robots.

This multidisciplinary work involves experts in paediatrics, biomedical engineering, robotics and neurorehabilitation to try to offer personalised solutions and support systems to professionals working in this field.

To this end, work is being done with aspects associated with biomedical engineering such as advanced sensors for physiological signals, artificial intelligence techniques and new human-like robots capable of teaching these children the meaning of different expressions and emotions, in what could be called personalised rehabilitation.

This technique is based on the application of knowledge acquired in the field of electrodermal activity sensors and heart rate variability or cameras for the detection of gestures and emotions in faces.

One of the cases that first comes to mind when we think of how technology can help in the arduous path of patient rehabilitation, whether due to an illness or an accident, is that of those who are unable to move due to a spinal cord injury.

Last February, the journal Nature Medicine reported the results of a Swiss team of researchers, part of an ongoing clinical trial, in which three people who had suffered a complete spinal cord injury and were paraplegic are now able to walk.

All this is possible thanks to an implant that stimulates the area of the spinal cord that controls the muscles of the trunk and legs, which works from an application that incorporates artificial intelligence.

These soft implants were placed under the vertebrae in contact with the spinal cord and are capable of modulating the neurons that regulate the activity of precise muscle groups, says neuroscientist Grgoire Courtine of the Ecole Polytechnique Fdrale de Lausanne (EPFL).

In this way, he adds, the spinal cord can be activated as the brain would naturally do for standing, walking, cycling or swimming.

The researchers combined this technology with a personalised computational framework to position the electrode palette to the needs of each patient, and thus personalise the activity stimulation programmes.

To better illustrate this process, we refer to one of the patients who received this technique, Michel Roccati, an Italian who four years ago had a motorbike accident and became completely paraplegic. Michel can now get up and walk using a walker in which two small remote controls are inserted.

A tablet sends the stimulation commands to a pacemaker in Michels abdomen, from which the stimuli are transmitted to the spinal implant to make Michel stand up.

The way the system works means that with a press of the button on the right side of his walker plus his willingness to activate his muscles, his left leg flexes and then positions a few centimetres further forward. When the left button is activated, the right leg takes a step in turn and starts to walk.

This system has also enabled him to go up and down stairs.

Another example of technology taking a decisive role in the peoples rehabilitation is the use of a bionic chip to enable an 88-year-old British woman who had lost the sight in her left eye to detect signals in that eye again.

The patient, a mother of seven and grandmother of eight, suffers from geographic atrophy, the most common form of age-related macular degeneration (AMD), a disease affecting more than five million people worldwide.

The operation, performed at Londons specialist Moorfields Eye Hospital, involved the surgical insertion of a two-millimetre microchip into the centre of the patients retina. The patient had to wear special glasses containing a video camera linked to a small computer, which in turn was attached to a waist band.

The chip captures the image provided by the glasses and transmits it to the computer, which uses artificial intelligence algorithms to process the information and guide the focus of the glasses.

Finally, the glasses project this image as an infra-red beam through the eye to the chip, which transforms it into an electrical signal that travels back through the cells of the retina to the brain. The latter, in turn, interprets this signal as if it were natural vision.

Just as we have been seeing during the COVID-19 pandemic all that technological tools and Artificial Intelligence are doing in health, their use in the field of patient rehabilitation is also meaning a step forward in improving peoples living conditions.

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Technology and Artificial Intelligence in support of rehabilitation - Telefnica

The Netherlands will host an international summit on the responsible application of artificial intelligence in the military domain in The Hague in February 2023.

The aim of the conference is to establish an agenda that will ultimately lead to international agreements on how to develop and apply this technology responsibly in this domain.

This international summit on Responsible AI in the Military Domain REAIM 2023 will be hosted in collaboration with the Netherlands Ministry of Defence at the World Forum in The Hague on 15 and 16 February.

The announcement was made at the United Nations General Assembly in New York by Minister of Foreign Affairs, Wopke Hoekstra.

Artificial intelligence (AI) is essential for high-tech, future-proof armed forces. At the same time, there are risks and dilemmas associated with it, for instance if the deployment of AI leads to lack of human control, the potential escalation of violence or lack of clarity as regards responsibility.

According to Mr. Hoekstra, AI is developing at a rapid pace, including in the military domain. This new technology is set to become one of the greatest challenges we will face in the area of international security and arms control. If we dont make international agreements now about frameworks for the responsible development, deployment and use of AI, we will regret it later.

The event will bring together representatives of governments, knowledge institutions, industry, thinktanks and civil society organisations from all over the world. In order to allow as many people as possible from these target groups to take part in the summit, some of the sessions will be streamed online. To launch the summit, anonline talk showwas held at the United Nations Headquarters in New York with Mr. Hoekstra taking part, alongside United Nations Under-Secretary-General and High Representative for Disarmament Affairs, Ms. Izumi Nakamitsu.

Bas Schot, Head of Hague Convention Bureau adds: Once again The Hague is hosting a summit that will lead to the long-term benefit of all. As the international city of peace and justice, it is only fitting we host a summit on the responsible use of arms and I look forward to seeing the legacy this event will bring.

Marije Bouwman, Director of Operations, Safety & Security, World Forum The Haguesaid,World Forum The Hague is honored to host this conference. We are thankful we can continue to build on our expertise of high-level secured events.

Image shows Minister Hoekstra with representatives from, amongst others, Chile, the United States, United Kingdom, France, South Korea, Germany, Turkey, Canada, Mexico, Australia, Indonesia at the launch of the REAIM 2023 summit in The Hague

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The Hague to Host Summit on Artificial Intelligence in the Military Domain - FTNnews.com

What GAO Found

Several machine learning (ML) technologies are available in the U.S. to assist with the diagnostic process. The resulting benefits include earlier detection of diseases; more consistent analysis of medical data; and increased access to care, particularly for underserved populations. GAO identified a variety of ML-based technologies for five selected diseases certain cancers, diabetic retinopathy, Alzheimers disease, heart disease, and COVID-19 with most technologies relying on data from imaging such as x-rays or magnetic resonance imaging (MRI). However, these ML technologies have generally not been widely adopted.

Academic, government, and private sector researchers are working to expand the capabilities of ML-based medical diagnostic technologies. In addition, GAO identified three broader emerging approachesautonomous, adaptive, and consumer-oriented ML-diagnosticsthat can be applied to diagnose a variety of diseases. These advances could enhance medical professionals capabilities and improve patient treatments but also have certain limitations. For example, adaptive technologies may improve accuracy by incorporating additional data to update themselves, but automatic incorporation of low-quality data may lead to inconsistent or poorer algorithmic performance.

Spectrum of adaptive algorithms

We identified several challenges affecting the development and adoption of ML in medical diagnostics:

These challenges affect various stakeholders including technology developers, medical providers, and patients, and may slow the development and adoption of these technologies.

GAO developed three policy options that could help address these challenges or enhance the benefits of ML diagnostic technologies. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. See below for a summary of the policy options and relevant opportunities and considerations.

Policy Options to Help Address Challenges or Enhance Benefits of ML Diagnostic Technologies

Evaluation (reportpage 28)

Policymakers could create incentives, guidance, or policies to encourage or require the evaluation of ML diagnostic technologies across a range of deployment conditions and demographics representative of the intended use.

This policy option could help address the challenge of demonstrating real world performance.

Data Access (reportpage 29)

Policymakers could develop or expand access to high-quality medical data to develop and test ML medical diagnostic technologies. Examples include standards for collecting and sharing data, creating data commons, or using incentives to encourage data sharing.

This policy option could help address the challenge of demonstrating real world performance.

Collaboration (reportpage 30)

Policymakers could promote collaboration among developers, providers, and regulators in the development and adoption of ML diagnostic technologies. For example, policymakers could convene multidisciplinary experts together in the design and development of these technologies through workshops and conferences.

This policy option could help address the challenges of meeting medical needs and addressing regulatory gaps.

Source: GAO. | GAO-22-104629

Diagnostic errors affect more than 12 million Americans each year, with aggregate costs likely in excess of $100 billion, according to a report by the Society to Improve Diagnosis in Medicine. ML, a subfield of artificial intelligence, has emerged as a powerful tool for solving complex problems in diverse domains, including medical diagnostics. However, challenges to the development and use of machine learning technologies in medical diagnostics raise technological, economic, and regulatory questions.

GAO was asked to conduct a technology assessment on the current and emerging uses of machine learning in medical diagnostics, as well as the challenges and policy implications of these technologies. This report discusses (1) currently available ML medical diagnostic technologies for five selected diseases, (2) emerging ML medical diagnostic technologies, (3) challenges affecting the development and adoption of ML technologies for medical diagnosis, and (4) policy options to help address these challenges.

GAO assessed available and emerging ML technologies; interviewed stakeholders from government, industry, and academia; convened a meeting of experts in collaboration with the National Academy of Medicine; and reviewed reports and scientific literature. GAO is identifying policy options in this report.

For more information, contact Karen L. Howard at (202) 512-6888 or howardk@gao.gov.

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Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning Technologies for Medical Diagnostics - Government Accountability...

The ramifications of advances in artificial intelligence (A.I.) are being felt further afield than anyone expected. A.I. perhaps entered the public consciousness in the 1990s thanks to chess competitions, but it's now infiltrating art competitions and, soon, the written word. Some commercial offerings can provide paragraphs of text based on brief prompts, keywords, and tone parameters. Users of Google's email service have, of course, been microdosing on A.I. since 2018, when Gmail rolled out Smart Compose.

What these developments bring home is that people in the so-called "creative class" are now facing the first-person reckoning that automation has long presented to blue-collar workers: Technology is going to radically change the way we work.

As an analyst at a think tank, my job consists of processing policy trends, formulating new ideas to tackle economic and social problems, and advancing them through the written word. If programs like Midjourney, DALL-E, and Voyager can already captivate human audiences, I haven't the slightest doubt that my modest ability to metabolize the policy landscape, reason my way to novel solutions, and manipulate language in provocative, engaging ways will soon be matchedand then surpassedby A.I. programs designed for the task.

While I am under no illusion that my work merits any blue ribbons, putting thoughts into words that persuade or stir emotion entails a certain artistry. It's an engrossing and gratifying process, one from which I derive identity. When I contemplate that a computer could soon do it better, I, like the Lancashire handloom weavers of the early 19th century, feel more than a bit threatened.

Garry Kasparov dealt with this conundrum two decades ago and has had a head start in managing the prospect of obsolescence. Kasparov, an all-time great chess player, had the distinction of holding the world title just at the same moment that computer chess programs ramped up their prowess. In 1996, Kasparov beat what was then the strongest chess engine ever created, IBM's Deep Blue. But as he recounts in his memoir, Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, he knew then that his reign would soon end. Indeed, in a 1997 rematch for which Kasparov was handsomely compensated, an updated Deep Blue brought the age of A.I. to global attention, dealing the champion a stunning defeat in the match's decisive sixth game.

In Deep Thinking, published in 2017, Kasparov explains how his perspective on A.I. has evolved and why. Despite the anguish the 1997 loss caused him, he views A.I. as one of the greatest opportunities for humanity to advance its well-being. The reason is that Kasparov has observed in the intervening years that the highest level of performance, on the chessboard and elsewhere, is reached when humans work with smart machines.

After Deep Blue's programmers established that it could see deeper into the game than the human mind, Kasparov and a group of partners came up with a new concept: What if instead of human vs. machine, people played against one another but with the assistance of chess software?

They called the new style of play "advanced chess," and the outcomes surprised Kasparov. It wasn't the player with the best chess software that necessarily won, nor was it the best human player. Rather, the top performers were the players who were able to use the machines most effectively, those who were able to get the most out of the chess engines and their own creative abilities.

Operating on the premise of Moravec's Paradox, i.e., where machines are strong is where humans are weak and vice versa, what Kasparov took away from the advanced chess experiment is that a clever working process beats both superior human talent and superior technological horsepower.

The same insight can be leveraged by artists, composers, writers, designers, and the like. Rather than viewing A.I. as the end of our livelihoods, we ought to see the opportunities it presents for better work.

For the creative class, the answer to the A.I. challenge is to make the most of the programs available to us. Is artistry lost because of A.I., or is it unlocked, as we are freed from some of the more formulaic structuring processes that drain energy? By delegating these aspects of creation to A.I., I anticipate having more mental space available to generate the rhetorical flourishes and the witty bits of embroidery that make writing enjoyable.

Yes, people deploying A.I. in the writing world, art competitions, and elsewhere will likely face scorn. But while a level playing field is appropriate in defined competitions, in open-ended fields to accuse a rival of cheating would be no more meaningful than in that of the textile industry. For the intrepid writer, A.I. will create opportunities to produce better work at a faster clip, just as the power loom did for the weavers of Lancashire.

Rather than fear, and certainly rather than Luddite suppression, this ought to be a moment of optimism. A.I. is coming for our jobs. Its arrival, however, will not be a harbinger of obsolescence but a catalyst for greater achievement.

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Artificial Intelligence Will Change JobsFor the Better - Reason

Corruption Perception Index (CPI) by Transparency International is the most widely used corruption index. And ironically, India has been constantly ranking only a few notches above the underdeveloped African countries. Corruption hinders the economic growth of a country preventing it from progressing in the right direction. Perhaps the legal procedure and laws have a narrow application for a diverse concept like corruption. Many a time it is highly impossible to differentiate between the so-called welfare measure from the very personal political agenda of the leaders. While financial and banking systems have been put under the scanner for corruption and fraudulent actors by global agencies like the world bank and IMF, corruption in other sectors is more than difficult to identify if not prevent. The organizational infrastructure cannot support the analysis of a vast amount of data and sift through it to connect the dots between different events to find malpractice.

Off late artificial intelligence is proving to be a savior of anti-corruption agencies world over. AI and machine learning, given that they can identify patterns in the data when applied to analyze inputs from different data points, could identify kinks from the mele of complex transactions. Automating public record searches can detect activities of corruption such as Money laundering, tax evasion, suspicious tenders, or bids in public procurements efficiently. However, until a few issues such as biased algorithms and data inadequacy are addressed, AIs automated systems cannot be set free in the open to catch the thief, in spite of the benefits like objectivity, efficiency and the cost-savings it offers.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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The Artificial Intelligence Pill for A World Vexed with Corruption - Analytics Insight

Chipotle's Chippy, an autonomous kitchen assistant that integrates culinary traditions with ... [+] artificial intelligence to make tortilla chips, is moving into the next phase of testing and will be integrated in a restaurant next month.

During Chipotles Q2 earnings call in late July, executives made it clear the system needed to refine some of its operational processes as dine-in business returns while off-premise business remains elevated.

In doing so, Chief Restaurant Officer Scott Boatwright touted the companys Project Square One, a game plan focused on employee training to execute orders more efficiently. Today, the company announced its also getting more technology involved.

Chipotle is testing two technologies specifically to streamline operations and reduce frictiona kitchen management system and an advanced location-based platform.

In eight Southern California restaurants, Chipotle is testing PreciTastes kitchen management system that provides demand-based cooking and ingredient preparation forecasts by leveraging artificial intelligence and machine learning. According to Chipotle, the system monitors ingredient levels in real time and notifies employees how much to prep, cook and when to start cooking. The system was created to not only optimize throughput but to also minimize food waste.

The new kitchen management system has alleviated manual tasks for our crew and given restaurant managers the tools they need to make informed in-the-moment decisions, ultimately enabling them to focus on an exceptional culinary and an outstanding guest experience, Chief Technology Officer Curt Garner said in a statement.

This isnt Chipotles first foray into AI. Earlier this year, Chipotle announced a test with Miso Robotics to bring its artificial intelligence-driven Chippy into its Cultivate [innovation] Center to replicate the chains signature tortilla chips. That test is now expanding, with Chippy making its first restaurant debut next month in a Fountain Valley, California, location.

From there, the company will gauge employee and guest feedback before developing a broader rollout plan.

During a recent interview, Garner said the company is looking at everything from internet of things to machine learning to run its restaurants more efficiently and enable crew members to focus on other tasks.

When you see us leaning into this space, it will be a question of are there better tools to help our crews versus removing a task? Those are the kind of things were looking at, Turner said.

The company is also currently testing Radius Networks Flybuy, a contextual restaurant program, at 73 Cleveland-area restaurants designed to identify Chipotle app users intent upon arrival. The location-based technology utilizes real-time data to let customers know their orders are ready, to remind them to scan the Chipotle Rewards QR code at checkout and more. It even alerts customers if theyre in the wrong pick-up location.

The program has yielded positive results so far, according to Chipotle, including improved in-store rewards engagement and delivery efficiencies.

Empowering our restaurants with advanced technologies is critical for operational excellence and better positions our teams for our ambitious growth plans, Boatright said in a statement.

Notably, Chipotle isnt the only chain exploring AI technology to improve operations. White Castle has been testing Miso Technologys Flippy in the back of the house for about two years, for instance, while Jamba has partnered with autonomous food platform Blendid to automate smoothies. Several restaurant chains, including Applebees, IHOP and Tropical Smoothie Cafe, leverage Flybuy.

In fact, a new survey from Capterra found that 76% of restaurants are currently using automation in three or more areas of operation, while 96% of restaurants are using some type of automation tool in the back of the house. As such, the cooking robotics space is expected to grow by over 16% a year through 2028 with an estimated worth of $322 million by 2028.

That said, Chipotles scale, company-owned model and zero-debt balance sheet adds a bit more intrigue to this trend. Chipotle has some latitude to pilot new solutions without franchisee investment or pushback, and any proven return on investment will likely provide a strong case for adoption across an industry still very much struggling with labor shortages.

Further, all of these technologies enhance throughput, a major focus for Chipotle to drive more sales. During the companys Q2 call, for example, CEO Brian Niccol said order fulfilment was in the low 30s on a per-15-minute basis nearly 10 years ago, which adds a full percent on comp sales on the day.

On a 15-minute basis, thats what were going after, he said during the earnings call.

Chipotles announcements today come on the heels of the companys Cultivate Next venture fund launch, created to identify strategically aligned companies for early-stage investments. As part of this $50 million fund, Chipotle has already invested in Hyphen, a foodservice platform that automates kitchen operations, and Meati Foods, a company that provides plant-based proteins.

Chipotle is also leveraging a new scheduling tool, has invested in autonomous delivery company, Nuro, and is testing radio-frequency identification to trace and track ingredients in its restaurants.

In a recent statement, Garner said the company is exploring investments in innovations that will enhance employee and guest experience and quite possibly revolutionize the restaurant industry.

Investing in forward-thinking ventures that are looking to drive meaningful change at scale will help accelerate Chipotles aggressive growth plans, he said.

Chipotle currently has about 3,000 locations, with plans to grow to about 7,000 in the coming years.

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Chipotle Is Testing More Artificial Intelligence Solutions To Improve Operations - Forbes

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Accounting Market Research Report by Component (Services and Solutions), Technology, Deployment, Application, Region (Americas, Asia-Pacific, and Europe, Middle East & Africa) - Global Forecast to 2027 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Accounting Market size was estimated at USD 1,556.52 million in 2021, USD 2,074.02 million in 2022, and is projected to grow at a CAGR 33.42% to reach USD 8,781.42 million by 2027.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Accounting Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Artificial Intelligence in Accounting Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence in Accounting Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence in Accounting Market?

4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence in Accounting Market?

5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence in Accounting Market?

6. What is the market share of the leading vendors in the Global Artificial Intelligence in Accounting Market?

7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence in Accounting Market?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/s6uz84

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Insights on the Artificial Intelligence in Accounting Global Market to 2027 - Rising Demand for Intelligent Accounting Processes Presents...

Journalists are automating processes and using systems that mimic human behavior to design tasks related to news gathering, content creation, distribution, marketing, and subscriptions in Latin America.

FREMONT, CA: Artificial intelligence (AI) has stopped merely being a science fiction component and has become a reality in recent years. Automation of processes and the creation of systems that mimic human behavior have reached journalism and are being used to design tasks of news gathering, content creation, distribution, marketing, and subscriptions. Automating processes and creating systems that mimic human behavior have reached journalism.

The use of AI is currently somewhat limited, despite its enormous potential, and the region is ravenous for information regarding the subject.

Insufficient funds and a need for training

The organization needs AI training in addition to producing articles for publication in Latin American media.

Ironically, although there is a great deal of understanding of the potential benefits of AI systems in the media, very little or even nothing has been done at the organizational level to implement these systems.

There is an unmistakable desire present in newsrooms to implement AI. The majority of the newsrooms had not taken advantage of the chance to implement AI into their day-to-day operations, despite its availability. The typical reaction was either a lack of resources or an absence of a corporate vision to adopt AI technologies as an integral part of the organization's future. Both of these issues were cited as the reason for the failure.

Similarly, the request made by journalists was to obtain training and trade experiences with other forms of media. The region is willing to enter the field of AI, but it needs basic information about the currently available prospects.

User loyalty

It would appear that the holy grail of reader monetization is the application of algorithms to make sense of data. Many news organizations in Latin America are either actively investigating this topic or are eager to do so. Particularly in the case of television networks, which are not accustomed to working with this kind of instrument.

The media are utilizing machine learning and AI to gain a deeper understanding of specific consumer habits, user loyalty, and the integration of advertising campaigns.

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Reasons behind the Limited Use of Artificial Intelligence in Latin American Media - CIOReview

Visiongain has published a new report entitled the Artificial Intelligence (AI) in Drug Discovery 2022-2032. It includes profiles of Artificial Intelligence (AI) in Drug Discovery and Forecasts Market Segment by Offering, (AI Software, AI Services) Market Segment by Technology, (Deep Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Other Technology) Market Segment by Applications, (Oncology, Infectious Diseases, Neurological Disorders, Metabolic Diseases, Cardiovascular Diseases, Other Applications) Market Segment by Type, (Target Identification, Molecule Screening, Drug Design and Drug Optimization, Preclinical and Clinical Testing) PLUS COVID-19 Impact Analysis and Recovery Pattern Analysis (V-shaped, W-shaped, U-shaped, L-shaped) Profiles of Leading Companies, Region and Country.

The global artificial intelligence (AI) in drug discovery market was valued at US$791 million in 2021 and is projected to grow at a CAGR of 30.7% during the forecast period 2022-2032.

In a Pharmacological Screen, AI Has a Stronger Prediction Power for Defining Relevant Interactions

AI makes use of the most recent developments in biology and computation to create cutting-edge drug discovery algorithms. AI has the potential to level the playing field in drug research, with to rapid increases in computing capacity and lower processing costs. In a pharmacological screen, AI has a stronger prediction power for defining relevant interactions. As a result, by carefully choosing the assay parameters in question, the risk of false positives can be decreased. Most crucially, AI has the ability to shift drug screening from the bench to a virtual lab, where results can be produced more quickly and intriguing targets can be prioritised without requiring extensive experimental input or personnel hours.

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Artificial Intelligence (AI) in Drug Discovery Market Report 2022-2032

How has COVID-19 had a significant negative impact on the Artificial Intelligence (AI) in Drug Discovery Market?

The COVID-19 pandemic presented a significant challenge to the pharmaceutical and bioanalytical communities in the creation of vaccines and therapies, as well as ongoing drug development activities. Existing procedures were tested to cope with reduced personnel at facilities and increased workloads for COVID-19-related study assistance, which included preclinical testing, clinical trial initiation, bioanalysis, and interactions with regulatory bodies, all in ultra-short timelines. Creative reimagining of procedures and the removal of barriers some of which had previously been regarded immovable were major factors in the project's success. Pharmaceutical firms working on antiviral medicines or vaccines have to deal with pandemic-related problems and alter their strategies in order to continue enrolling patients in existing clinical studies and developing new treatments and cures. The remainder of this essay focuses on bioanalysis and drug development issues and lessons learned.

How this Report Will Benefit you?

Visiongains 483 page report provides 270 tables and 264 charts/graphs. Our new study is suitable for anyone requiring commercial, in-depth analyses for the global artificial intelligence (AI) in drug discovery market, along with detailed segment analysis in the market. Our new study will help you evaluate the overall global and regional market for Artificial Intelligence (AI) in Drug Discovery. Get the financial analysis of the overall market and different segments including type, technology, application, offering and capture higher market share. We believe that high opportunity remains in this fast-growing artificial intelligence (AI) in drug discovery market. See how to use the existing and upcoming opportunities in this market to gain revenue benefits in the near future. Moreover, the report would help you to improve your strategic decision-making, allowing you to frame growth strategies, reinforce the analysis of other market players, and maximise the productivity of the company.

What are the current market drivers?

AI Utilizes the Latest Advances in Biology and Computing to Develop State-of-the-Art Algorithms for Drug Discovery

With the introduction of artificial intelligence (AI) and machine learning, the pharmaceutical business is undergoing a significant transformation. While many see this new technology as a potential threat, it could actually be the solution to our persistent prescription shortages. Indeed, AI has already proven to be useful in several aspects of drug discovery and development, from assisting scientists in finding new potential treatments to forecasting which pharmaceuticals will fail clinical trials. There's no doubt that these technologies will have a huge impact on the future of medicine as more pharma companies adopt them.

Thanks to AI, the Cost and Timelines of Developing a New Treatment Will Be Rewritten

The cost of discovering a new medicine is estimated to be in the billions of dollars. A huge percentage of the money invested on the nine out of ten proposed new therapy discoveries that fail somewhere between clinical trial phase I and regulatory approval goes down the drain. Surprisingly, few in the industry doubt the value of doing things differently. Many prominent pharma companies believe that the answer is within grasp when assisted by cutting-edge technology. Using the supercomputer IBM Watson, Pfizer uses machine learning (ML) or deep learning (DL) and breakthroughs to develop immuno-oncology medications.

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Artificial Intelligence (AI) in Drug Discovery Market Report 2022-2032

Where are the market opportunities?

A New Wave of Drug Discovery is Setting the Ideal for the World

Small-molecule drug discovery can benefit from AI in four ways: new biology, improved or original chemistry, higher success rates, and faster and cheaper discovery processes. Many issues and limits in traditional R&D can be addressed with this technique. Each tool provides drug research teams with new insights and, in some circumstances, can completely transform long-standing operations. Understanding and distinguishing between use cases is crucial because these technologies are applicable to a number of discovery scenarios and biological targets.

AI Has Received a Lot of Attention in the Pharmaceutical Business for Medication Discovery and Development

Within the pharmaceutical industry, there has been considerable focus on AI for drug discovery and development. The "AI for Drug Discovery" business includes research organisations, AI innovators both early-stage and well-funded biotech companies, and multinational pharma giants. Though artificial intelligence (AI) has only recently gained traction in the industry, computational methods to drug development particularly in chemistry and biology have a long history that predates electronic computing.

Competitive Landscape

The major players operating in the artificial intelligence (AI) in drug discovery market are Atomwise, Benevolent AI, Berg Health, Bioage, Biosymetrics, Cloud Pharmaceuticals, Cyclica, Deep Genomics, DeepMind, Envisagenics, Euretos, Exscientia, GNS Healthcare, IBM Corporation, Insilico Medicine, These major players operating in this market have adopted various strategies comprising M&A, investment in R&D, collaborations, partnerships, regional business expansion, and new product launch.

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About Visiongain

Visiongain is one of the fastest-growing and most innovative independent market intelligence around, the company publishes hundreds of market research reports which it adds to its extensive portfolio each year. These reports offer in-depth analysis across 18 industries worldwide. The reports cover a 10 year forecasts, are hundreds of pages long, with in depth market analysis and valuable competitive intelligence data. Visiongain works across a range of vertical markets, which currently can influence one another, these markets include automotive, aviation, chemicals, cyber, defence, energy, food & drink, materials, packaging, pharmaceutical and utilities sectors. Our customized and syndicatedmarket research reportsmeans that you can have a bespoke piece of market intelligence customized to your very own business needs.

Contact:Dev VisavadiaPR at Visiongain Reports LimitedTel: + 44 0207 336 6100Email: dev.visavadia@visiongain.com

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Global Artificial Intelligence (AI) in Drug Discovery market is projected to grow at a CAGR of 30.7% By 2032: Visiongain Reports Ltd - GlobeNewswire