Category Archives: Ai

As schools welcome back students, many educators and administrators are depending on bots to alert them of kids who are at risk of harming themselves or others.

School districts use artificial-intelligence software that can scan student communications and web searches on school-issued devicesand even devices that are logged in via school networksfor signs of suicidal ideation, violence against fellow students, bullying and more. Included in the scans are emails and chats between friends, as well as student musings composed in Google Docs or Microsoft Word.

When the AI recognizes certain key phrases, these systems typically send an alert to school administrators and counselors, who then determine whether an intervention with the student and parents is warranted.

Many school districts have used monitoring software over the past three years to prevent school shootings, but it has evolved to become a tool to spot a range of mental-health issues, including anxiety, depression and eating disorders. School administrators say such surveillance is more important than ever as students return to the classroom after 18 months of pandemic-related stress, uncertainty and loss. Critics say it raises questions about privacy, misuse and students ability to express feelings freely or search for answers.

I asked an expert in childrens mental health to provide some ways to talk to your kids about this before you get a call from the school. Ive compiled them below (in case you just want to skip down for the parenting advice).

Surveillance of student communications and web searches appears to be fairly widespread. In June, the consumer-advocacy nonprofit Center for Democracy and Technology conducted online surveys of more than 1,000 third- through 10th-grade teachers, more than 1,600 parents and more than 400 high-school students across the country. The topic was student-monitoring software. According to the results, which the organization plans to release Tuesday, 81% of the teachers surveyed said their school uses some form of monitoring software, and 77% of the students said the same. Of those students, 80% said knowing they were being monitored made them more careful about what they searched online.

In some cases, its unclear whether students understand they are being monitored. Some schools disclose it in tech-use policies or codes of conduct, but how many kids actually read those? The Springfield School District, in Oregon, mentions monitoring in the policy students must sign when checking out school-issued laptops, but the district doesnt trumpet it.

I dont want to be sneaky about it, but if we were really obvious about it, students might not use their school devices," said Brian Megert, special programs director for the district, which this fall began using school online-safety company Lightspeed Systems to monitor student communications. Lightspeeds monitoring software is used in nearly 32,000 U.S. schools at an annual cost of about $2 a student.

From a public-sector perspective, there is no presumed anonymity in anything you do on a school device, on a school network or in a school setting," Dr. Megert added. I have mixed feelings about it, but if were going to err on one side it has to be on the side of safety."

Just last weekend, Lightspeed flagged a Google chat that a student in Dr. Megerts district had with a suicide hotline, as well as chats another student had with peers about plans for self-harm. In both cases, the school contacted the students families and arranged for mental-health services.

The San Marcos Unified School District near San Diego received five legitimate alerts of self-harm from Lightspeed in the first two weeks of school. The districts assistant superintendent, Tiffany Campbell, said that is an unusually high number this early in the school year.

Intellectually we knew how much support our students would need, but its a hard reality to see," Dr. Campbell said.

She said the majority of alerts the district receives are false alarms, in which a problematic phrase is part of an assignment, or just jokes among friends. However, she estimates that 20% to 30% of the alerts indicate potential crises.

Dr. Campbell described a recent case involving a student who kept a personal diary in a Google Doc, stating a desire to die. An administrator was immediately able to call the parent, and the parent immediately got their child help," she said. Thats the type of situation that makes the program worth it."

False alarms might decrease now that Lightspeed has added human reviewers to look at flagged communications and assign them a threat level.

Bark, another online monitoring company, said it detected 5,000 credible self-harm or suicide situations in the second week of September alone. Bark, available to schools at no charge, is used by more than 2,900 school districts and doesnt typically include human review.

Its hard to argue against efforts to save kids lives. But privacy and mental-health experts say such surveillance can be a slippery slope, especially if it ends up being used for reasons other than harm prevention.

Beila Lugo, mental health coordinator for Charles County Public Schools in Maryland, which uses Barks software, said she has had to tell some administrators to back off when they were planning to confront students for inappropriate language or content in some flagged communications. Were not using this for discipline, were using it for monitoring," she said.

Privacy advocates and mental-health experts say this kind of monitoring might take away the only safe space that some kids, especially in poorer families, have to search for help and to communicate with friends.

The school Chromebook is the only device some kids have, and the school Wi-Fi is the only internet connection some have," said Sophia Cope, a senior staff attorney with the Electronic Frontier Foundation.

What You Can Do

Privacy is a developmental milestone for teens," said Hina Talib, an associate professor of pediatrics and an adolescent-medicine specialist at Childrens Hospital at Montefiore in New York. By acknowledging that, she added, we give them choices and respect." Still, she said, there are ways to talk to kids about mental health before you get a call from the school.

Start early. Even very young kids can have thoughts of self-harm. Bark has detected increased expressions of suicidal ideation among elementary-school students so far this school year. Dr. Talib suggests checking in with kids about mental health starting in fifth grade.

Dont be afraid to talk about suicide. Dr. Talib said some parents worry that bringing up the topic of self-harm or suicide could inspire kids to act, but she said that isnt true; kids usually feel relieved to have someone to talk to.

Find a conversation starter. Rather than directly asking children how theyre doing, it can be helpful to find a reason to broach the topic. Kids might be aware that September is Suicide Prevention Awareness Month. You can talk about that, and ask what kinds of preventive steps they think would be helpful. Parents can also raise the topic in the context of a schools surveillance tech. When kids are asked their opinion, it helps them open up, Dr. Talib said.

Ask about their peers. Instead of making the conversation about them, a good way to get into a discussion is to ask about others. Dr. Talib suggests saying something like, Have you ever heard of anyone who cut themselves and you werent sure what that was about? Im happy to talk to you about it."

Make helplines available. Along with numbers of neighbors or relatives on the fridge, Dr. Talib said, you can post the number to the National Suicide Prevention Lifeline (800-273-8255) or the Crisis Text Line (Text HOME to 741741) so that your child knows theres someone to call or text if they need help.

Bring in a neutral party. If you suspect your child is struggling but you dont think your child will open up to you, Dr. Talib suggests asking a trusted third party to check in with your childit could be a coach, a teacher or a relative.

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How to detect your childs emotional distress before the schools AI does - Livemint

Ronen Lavi and Shay Perera have spent years working to develop and deploy AI in one of the most demanding environments in the worldthe elite intelligence units of the Israel Defense Forces (IDF). Lavi established and led the AI Lab of Israels Military Intelligence and Perera served there as manager of machine learning and computer vision research and development.

After being awarded a National Security Award in 2018, they left the IDF to launch a startup, as many Israelis with similar experience and skills have done before them. But in their case, it wasnt a cybersecurity or fintech or proptech startup, but a healthcare startup, joining the most recent thriving sector in the booming Startup Nation of Israel.

Navina Founders CEO Ronen Lavi (left), CTO Shay Perera (right)

The rapid digital transformation of the healthcare industry worldwide, the proliferation of healthcare data, the increasing complexity of healthcare (including its administration), the dearth of qualified personneland the Covid pandemichave all contributed to a rising demand for AI solutions, intended to assist with detection, diagnosis, treatment, preventive care and wellness.

The wealth of data that is produced by digitized medical records is what modern AI approaches (deep learning) require so they can learn from examples, automate certain decisions, and provide a helping hand to physicians and healthcare staff. But it also contributes to a data overload that is simply impossible to digest. The intuitive belief that more data is inherently a good thing, is a misguided notion. Without sense-making tools, more data doesnt mean better patient understanding, says Perera, Navinas CTO.

The data overload frustrates physicians and causes burnout. A recent study concluded that physician burnout has negative consequences for physician wellness, patient care, and the health care system and that 50% of physicians in the United States experience burnout, now considered by many experts to be an epidemic.

The data overload lands on overburdened physicians at a critical moment. There are thousands of data points that physicians need to review in a few minutes before seeing the patient, says Perera. The patient portrait Navina provides is a one-page summary, extracting critical information from many sources, including PDFs or images of faxes that the physicians are hard-pressed to search. We teach the machine how to understand the different languages of the different data sources, and how to connect the dots so the physicians get a summary document in one language they can understand, says Perera.

To accomplish this, Navina developed NLP (natural language processing) models which extract and structurethe data into medical ontologies, and using deep learning, Navina analyzes it to associate each piece of information with a specific medical terminology code.

In addition, Navina has developed a proprietary medical knowledge graph, which it uses to connect the different medical ontologies.The knowledge graph is based both on medical literature, and on Navinas state-of-the art datasets, curated by its professional team of medical doctors, and used for training Navinas machine learning models. Once trained, Navinas AI engine can provide links between diagnoses, medications, labs, vitals, consult notes, imaging and more. These medically guided maps allow it to provide alerts regarding missed diagnoses, abnormal results, missing labs, and missing tests.

This is a great example of what AI pioneer Andrew Ng has recently called data-centric AI, urging the improvement in the quality of the data used to train AI programs and building the tools and processes required to put data at the center of developers work. Especially in healthcare, where the data sets are relatively small, the quality of the data and making sense of it are crucial for the success of AI-driven solutions.

Navina is not only data-centric, patient data-centric, it is first and foremost, physician-centric. Addressing the specific pain points and business needs of the intended customers is important for the success of any new venture, but especially so in healthcare. It is a market with numerous individual decision-makersphysicians and medical practices. It is a market that has been notoriously slow to adopt new, computer-based tools.

Lavi, Navinas CEO, says his and Pereras experience in the army with the introduction of new technologies has been a great help for them. The only way to succeed it is to go from the bottom up, to work with your users from day one, he says. With Navina, they started working directly with physicians and found the right design partners, such as the American Academy of Family Physicians. Build it step by step, understand the workflow and the right accelerators, get the physicians on board, put the physician in the center and then get the right economics from it, Lavi adds.

The right economics have a lot to do with the shift in the U.S. to value-based healthcare. This is a healthcare delivery model in which providers are paid based on patient health outcomes and are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives.

In this new model, Navina helps physicians increase their reimbursements by ensuring that the right coding for specific conditions is applied to each patient. For example, the Centers for Medicare & Medicaid Service (CMS) risk adjustment model calculates risk scores for Medicare Advantage patients. A Risk Adjustment Factor (RAF) is assigned to each eligible Medicare Advantage beneficiary, based on their health conditions and other factors. Higher risk scores represent patients with a greater than average disease burden.

The detection (and coding) of chronic conditions and their treatment, for example, assisted by Navinas AI, could translate into thousands of dollars in additional monthly income for providers (in addition to reducing the administrative burden on physicians and coders).

After three months of deployment at Northern Ohio Medical Specialists (NOMS Healthcare), an independent physician group with over 250 providers and 30 specialties, NOMS has seen a significant increase in HCC-RAF (Risk Adjustment Factor) scores, which is expected to translate into several million dollars in revenue over the upcoming calendar years.

Announcing the results of this deployment, Jennifer Hohman, a family physician and NOMS Board Chair, said that using Navina is like having another physician at my side. A partner who can instantly read through the entire record - including every consult or discharge note - and then give me only the data I need, so that I dont miss anything.

To gain initial feedback from physicians and use it to improve the AI engine, Navina is first targeting medium-size practices, says Lavi, but it is already working with larger ones. Perera credits their success in deploying their software in just three months to their use of AWS resources to accelerate the process, citing AWSs scalable solutions specifically tailored to healthcare and its security and access control functionality.

Jared Saul, Global Lead at AWS for Healthcare & Life Sciences Startups and Investors, concurs: AWS provides the cloud infrastructure and advanced services that help Navina to turn millions of structured and unstructured patient records into clear, actionable diagnostic summaries, which have the potential to make a huge difference for doctors and patients.

Lavi and Perera say they are excited to see their startup participate in the larger movement of healthcare transformation, a movement encouraging data sharing, successful deployment of new and innovative practices, and using AI to improve healthcare delivery.

More:

AI Startup Navina Leverages The Amazon Cloud To Improve Patient Care - Forbes

The pharmaceuticals firm GSK has struck a five-year partnership with Kings College London to use artificial intelligence to develop personalised treatments for cancer by investigating the role played by genetics in the disease.

The tie-up, which involves 10 of the drug makers artificial intelligence experts working with 10 oncology specialists from Kings across their labs, will use computing to play chess with cancer, working out why only a fifth of patients respond well to immuno-oncology treatments.

Dr Kim Branson, the global head of artificial intelligence and machine learning at GSK, said only 20% of patients respond well to the new oncology drugs that harness the bodys immune system to fight cancer.

Sometimes it works like a game buster and it wipes out the cancer. Wed like that to work all the time. This could be transformative, Branson said.

The partnership will use GSKs cancer drugs to start with and initially focus on solid cancers such as thoracic malignancies, gastrointestinal and womens cancers. Hopefully well create a framework that other people can contribute to, Branson said.

GSK and other large drug makers have been investing in AI to mine the vast quantities of data available to develop new medicines, pinpoint why some people are susceptible to certain diseases, and improve and personalise patient care.

AI uses algorithms to carry out tasks, with computers learning through repetitive processes rather than instruction from humans. The team will use a 3D cellular model of a patients disease to study how tumour cells from the patients undergoing treatment interact with immune cells.

What if we could play chess with the cancer? Branson said. Cancer is a tricky thing. You treat with X, then you see resistance. The tumour says, You do that, Im going to respond with this. Were using the predictive power of AI to think of potential strategies to outmanoeuvre disease. Our partnership with Kings can make this a possibility.

The team will monitor for dynamic biomarkers molecules found in blood, other body fluids or tissues that are a sign of disease that can predict resistance during treatment or a later relapse. The research partnership is based on a novel machine learning model that integrates multimodal data, genetic and molecular traits, tumour location, images and biomarker blood tests.

Prof Tony Ng, head of the comprehensive cancer centre at Kings, said that in general half of cancer patients who were clinically diagnosed to have advanced but operable cancers came back within one to two years after treatment, such as chemotherapy, when it was discovered that the cancer had spread to other parts of the body.

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To identify those at high risk, the team will create a digital biological twin of the patient, to test multiple drugs, and multiple doses, at multiple time points.

We are linking up the patient with the twin and can immediately feed back info to the clinical trial or clinical management algorithms, Ng said. The biological twin will not only tell us this person has a high risk, but also what we as oncologists do about it.

Ng added that different parameters besides genomics can be looked at within the twin, such as whether the immune system is suppressed through contact with cancer cells (quantified by new imaging methods), to develop a multimodal monitoring tool. Over the five years, the team hope to create specialist equipment.

Branson said the partnership could, if necessary, use the UKs most powerful supercomputer, developed by the US-based firm Nvidia, which became operational in July. The Cambridge-1 deploys AI methods and is available to a range of organisations, including GSK and Kings.

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GSK teams with Kings College to use AI to fight cancer - The Guardian

Artificial Intelligence is jockeying to become the focal point of U.S. technology innovation in coming years, and San Diego is among the cities well positioned to be a frontrunner in this looming AI race.

A new report from the Metropolitan Policy Program at the Brookings Institution ranked more than 360 cities based on their AI economic prowess.

Bay Area metros San Francisco and San Jose- topped the list, according to Brookings, a public policy think tank based in Washington, D.C. They were followed by 13 earlier adopter cities that managed to claw out a toehold in AI, including San Diego.

Not everywhere should be looking to artificial intelligence for a major change in its economy, but places like San Diego really need to, said Mark Muro, a Brookings fellow and co-author of the report. I think the costs of being out of position on it are pretty high for San Diego, and the benefits of leveraging it fully are really high.

To rank cities, Brookings combined data on federal research grants, AI academic papers, AI patents, job postings and AI-related companies, among other factors.

Besides San Diego, Los Angeles, Seattle, Boston, Austin, Washington, D.C., and Raleigh, N.C., are in strong positions. Smaller cities with significant AI footprints relative to their size include Santa Barbara, Santa Cruz, Boulder, Colo., Lincoln, Neb., and Santa Fe, N.M.

An additional 87 cities have the potential to become players but so far have limited AI activities, according to the study.

For most of us. AI is best known through recommendations that pop up on Amazon or Spotify, when smart speakers answer voice commands, or when navigation apps give turn-by-turn directions.

But AI is much more than that, with the potential to permeate thousands of industries. It could prevent power outages and help heal grids quickly, better route shipping to cut emissions, aid in medical diagnoses, and power navigation for self-driving vehicles.

Muro said Brookings undertook the research after receiving requests from economic development officials.

They watched the digitization of everything during the pandemic, he said. Theyre asking where do we stand on these advanced digital technologies? How do we engage with this?

As with other technologies, artificial intelligence tends to be clustered on the coasts. Of the 363 metro areas in the study, 261 had no significant AI footprint.

This is not everywhere, said Muro. But we think there can be a happy medium where we retain our coastal innovation centers while also taking steps to help other places make progress and counter some of this massive concentration.

In San Diego, companies such as Qualcomm, Oracle, Intuit, Teradata, Cubic, Viasat, Thermo Fisher and Illumina develop artificial intelligence and machine learning algorithms.

But key drivers of the regions AI prowess stems from the military and universities.

The Naval Information Warfare Systems Command (NAVWAR) is based locally, creating a magnet for defense contractors and cyber security firms working in AI.

San Diegos affiliation with the military has been extremely important, said Nate Kelley, senior researcher at the San Diego Regional Economic Development Corp. There are more and more contracts coming, particularly through NAVWAR. Those federal contracts tend to be large, and theyre multi-year. So, theyre less vulnerable to business cycles.

UC San Diego was an early researcher in neural networks, said Rajesh Gupta, director of the Halicioglu Data Sciences Institute. That work helped pave the way for the machine learning engines that banks use to uncover credit card transaction fraud.

Gupta thinks the Brookings report underestimates San Diegos AI capabilities. This summer, a new AI Research Institute at UCSD won a $20 million grant from the National Science Foundation to tackle big, complicated problems.

Among them: tapping artificial intelligence to cut the time and cost of designing semiconductors; finding ways to improve communications networks; and researching how robots interact with humans to make self-driving cars safer.

The San Diego Super Computer Center also performs research related to AI, and the San Diego Association of Governments (SANDAG) has been an early proponent of AI-based smart cities technologies, said Gupta.

We have a $39 million effort going on today basically on grid response and making it intelligent, said Gupta. Its smart buildings, smart parking, smart transportation. These are what will define the metropolitan areas of tomorrow with AI embedded in them.

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San Diego ranks relatively high in national ranking for artificial intelligence innovation - The San Diego Union-Tribune

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Since the invention of computers or machines, their capability to perform various tasks went on growing exponentially. Humans have developed the power of computer systems in terms of their diverse working domains, their increasing speed, and reducing size with respect to time.

A branch of Computer Science named Artificial Intelligence pursues creating the computers or machines as intelligent as human beings.

According to the father of Artificial Intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

AI is accomplished by studying how human brain thinks, and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems.

While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, Can a machine think and behave like humans do?

Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.

To Create Expert Systems The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advice its users.

To Implement Human Intelligence in Machines Creating systems that understand, think, learn, and behave like humans.

Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem solving.

Out of the following areas, one or multiple areas can contribute to build an intelligent system.

The programming without and with AI is different in following ways

In the real world, the knowledge has some unwelcomed properties

AI Technique is a manner to organize and use the knowledge efficiently in such a way that

AI techniques elevate the speed of execution of the complex program it is equipped with.

AI has been dominant in various fields such as

Gaming AI plays crucial role in strategic games such as chess, poker, tic-tac-toe, etc., where machine can think of large number of possible positions based on heuristic knowledge.

Natural Language Processing It is possible to interact with the computer that understands natural language spoken by humans.

Expert Systems There are some applications which integrate machine, software, and special information to impart reasoning and advising. They provide explanation and advice to the users.

Vision Systems These systems understand, interpret, and comprehend visual input on the computer. For example,

A spying aeroplane takes photographs, which are used to figure out spatial information or map of the areas.

Doctors use clinical expert system to diagnose the patient.

Police use computer software that can recognize the face of criminal with the stored portrait made by forensic artist.

Speech Recognition Some intelligent systems are capable of hearing and comprehending the language in terms of sentences and their meanings while a human talks to it. It can handle different accents, slang words, noise in the background, change in humans noise due to cold, etc.

Handwriting Recognition The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text.

Intelligent Robots Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, to exhibit intelligence. In addition, they are capable of learning from their mistakes and they can adapt to the new environment.

Here is the history of AI during 20th century

Karel apek play named Rossum's Universal Robots (RUR) opens in London, first use of the word "robot" in English.

Foundations for neural networks laid.

Isaac Asimov, a Columbia University alumni, coined the term Robotics.

Alan Turing introduced Turing Test for evaluation of intelligence and published Computing Machinery and Intelligence. Claude Shannon published Detailed Analysis of Chess Playing as a search.

John McCarthy coined the term Artificial Intelligence. Demonstration of the first running AI program at Carnegie Mellon University.

John McCarthy invents LISP programming language for AI.

Danny Bobrow's dissertation at MIT showed that computers can understand natural language well enough to solve algebra word problems correctly.

Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on a dialogue in English.

Scientists at Stanford Research Institute Developed Shakey, a robot, equipped with locomotion, perception, and problem solving.

The Assembly Robotics group at Edinburgh University built Freddy, the Famous Scottish Robot, capable of using vision to locate and assemble models.

The first computer-controlled autonomous vehicle, Stanford Cart, was built.

Harold Cohen created and demonstrated the drawing program, Aaron.

Major advances in all areas of AI

The Deep Blue Chess Program beats the then world chess champion, Garry Kasparov.

Interactive robot pets become commercially available. MIT displays Kismet, a robot with a face that expresses emotions. The robot Nomad explores remote regions of Antarctica and locates meteorites.

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Artificial Intelligence - Overview

INDUSTRY INSIGHT

The pandemic has put a spotlight on how big data and analytics technologies are being used in the public health sector.

A prime example of this?Contact tracing, where phone numbers and location data from mobile devices were combined with lab results in public health systems to issue alerts when an individual came in contact with a confirmed COVID patient.This information empowered people to preemptively self-isolate and/or head for rapid testing. Google and Apple, meanwhile, developed some groundbreaking application programming applications (APIs) for contact tracing that protected anonymity, while allowing their devices to receive updates from state disease surveillance systems and send out alerts.

The use of big data during the pandemic is certainly a harbinger of things to come, and public health agencies must understand how such data is being used. They should start working on plans to protect the privacy of the end user and comply with the evolving laws around personal data privacy.

Additionally, organizations should determine what theyll do with the data theyre gathering.Of course, all the data in the world is worthless without the right tools to read and interpret it. Artificial intelligence is vital for processing the vast amounts of data collected by todays technology.It has powered everything from tracking the initial spread of the outbreak to helping researchers quickly analyze and interpret huge amounts of data to come up with a vaccine. Going forward, AI and big data will be vital to analyzing vaccine efficacy, identifying breakthrough case trends and more.

Targeted outreach and prevention

Big data and AI have been foundational technologies for other programs. During the pandemic, data has been used for targeted outreach and prevention efforts, especially during the vaccine rollout. The ability to recognize trends in a cohort or region allowed for more effective risk mitigation. For immunization information systems (IIS), this meant parsing data to identify and prioritize groups who are at the most risk from a lack of vaccination. The age-bracketed rollout of the vaccine is a good example of this. Yet, new insights from ongoing data analytics efforts will help micro-target more at-risk groups as time goes on.

Identifying at-risk groups is just one part of the pandemic response process. From there, it involves what are essentially next-generation logistics efforts: monitoring vaccine distribution, managing vaccine appointments and tracking the growing numbers of vaccinated individuals.This efforts feature an incredible number of moving parts: government and public health offices, vaccine providers, health care workers and more.Marketing and education components will also see data analytics play an important role in their efforts.

All of which places a high data load on systems -- a load which requires modern architectures and flexibility to manage. Unfortunately, most public health offices today are using outdated systems that were designed for managing a load about 10 times smaller than what they're forced to deal with now.

Cloud computing can help public health agencies scale up to accommodate the new data load, with architectures that auto-scale and adapt to changing flows.But the systems themselves must also be architected to support the horizontal scaling enabled by cloud computing.

Stateless architecturesand BPMN 2.0

Newer architecturesare designed for this sort of flexibility. Called "stateless applications,"these architectures dont storetheirstate on the serveranddontneed to know the history of what was happening on the system,allowingorganizations toadd more servers to scale up and meet demand.The pandemic served as a powerful reminder of just howfastthings can change.Stateless applications are the ideal way to keep up withevolving requirements and mandates, allowing agencies to implement new functional changesquickly and easily.

Along with the versatility of stateless architectures, public health organizations should be leveraging Business Process Management Notation 2.0. This notation method allows systems to take in, change and adapt to new requirements with ease. One of the major stresses placed on health systems during the pandemic was that new requirements from the Centers for Disease Control and Prevention didnt necessarily fit into the outdated systems public health offices used. Agencies then had to manually solve for many new processes. BPMN 2.0 avoids that problem altogether, saving countless hours of work and ensuring a higher level of compliance.

Sharing data among multiple entities

Science depends on reliable data, but it has traditionally been a rare occurrence for health care data to be shared among multiple entities. Data privacy concerns are one of the main reasons for this siloed approach. However, those silos started coming down as health care researchers and public health agencies around the world started collaborating during the pandemic.

REST-based APIs can serve an important function here. Public health agencies have relied on the HL7 industry standard for sharing health informationviaAPIs, but before the COVID outbreak many of these agencies were transitioning to the updated FHIR standard, which offersmore functionality and flexibilitythan HL7.During the pandemic, manyof thesetransition projects went on hold.Its now time toget back to implementingFHIR in order to meet public certification requirements.

Sharing health care data is a new trend, and an exciting one. As AI makes it easier to provide meaningful data ownership and protect personal data privacy, it facilitates collaboration by multiple entities on shared data. This in turn spurs innovation, allowing the best minds in science to work together toward a better future. For IIS, this means sharing ideas about what works that will lead to new best practices across organizations.

Big data, analytics and AI allow public health organizations to respond rapidly to public health emergencies, which potentially translates into lives saved.

And thats the big goal for all of these innovative new approaches for IIS: save lives and improve public health. Todays developing trends will help mitigate the effects of the next pandemic and result in a safer future for all.

About the Author

John Schaeffer is CEO of SSG, a health care technology and information services company.

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How data, analytics and AI power public health - GCN.com

Ash Fontana, a managing director at Zetta Ventures, is the author of The AI-First Company: How to Compete and Win with Artificial Intelligence.More posts by this contributor

Investors in AI-first technology companies serving the defense industry, such as Palantir, Primer and Anduril, are doing well. Anduril, for one, reached a valuation of over $4 billion in less than four years. Many other companies that build general-purpose, AI-first technologies such as image labeling receive large (undisclosed) portions of their revenue from the defense industry.

Investors in AI-first technology companies that arent even intended to serve the defense industry often find that these firms eventually (and sometimes inadvertently) help other powerful institutions, such as police forces, municipal agencies and media companies, prosecute their duties.

Most do a lot of good work, such as DataRobot helping agencies understand the spread of COVID, HASH running simulations of vaccine distribution or Lilt making school communications available to immigrant parents in a U.S. school district.

However, there are also some less positive examples technology made by Israeli cyber-intelligence firm NSO was used to hack 37 smartphones belonging to journalists, human-rights activists, business executives and the fiance of murdered Saudi journalist Jamal Khashoggi, according to a report by The Washington Post and 16 media partners. The report claims the phones were on a list of over 50,000 numbers based in countries that surveil their citizens and are known to have hired the services of the Israeli firm.

Investors in these companies may now be asked challenging questions by other founders, limited partners and governments about whether the technology is too powerful, enables too much or is applied too broadly. These are questions of degree, but are sometimes not even asked upon making an investment.

Ive had the privilege of talking to a lot of people with lots of perspectives CEOs of big companies, founders of (currently!) small companies and politicians since publishing The AI-First Company and investing in such firms for the better part of a decade. Ive been getting one important question over and over again: How do investors ensure that the startups in which they invest responsibly apply AI?

Lets be frank: Its easy for startup investors to hand-wave away such an important question by saying something like, Its so hard to tell when we invest. Startups are nascent forms of something to come. However, AI-first startups are working with something powerful from day one: Tools that allow leverage far beyond our physical, intellectual and temporal reach.

AI not only gives people the ability to put their hands around heavier objects (robots) or get their heads around more data (analytics), it also gives them the ability to bend their minds around time (predictions). When people can make predictions and learn as they play out, they can learn fast. When people can learn fast, they can act fast.

Like any tool, one can use these tools for good or for bad. You can use a rock to build a house or you can throw it at someone. You can use gunpowder for beautiful fireworks or firing bullets.

Substantially similar, AI-based computer vision models can be used to figure out the moves of a dance group or a terrorist group. AI-powered drones can aim a camera at us while going off ski jumps, but they can also aim a gun at us.

This article covers the basics, metrics and politics of responsibly investing in AI-first companies.

Investors in and board members of AI-first companies must take at least partial responsibility for the decisions of the companies in which they invest.

Investors influence founders, whether they intend to or not. Founders constantly ask investors about what products to build, which customers to approach and which deals to execute. They do this to learn and improve their chances of winning. They also do this, in part, to keep investors engaged and informed because they may be a valuable source of capital.

Investors can think that theyre operating in an entirely Socratic way, as a sounding board for founders, but the reality is that they influence key decisions even by just asking questions, let alone giving specific advice on what to build, how to sell it and how much to charge. This is why investors need their own framework for responsibly investing in AI, lest they influence a bad outcome.

Board members have input on key strategic decisions legally and practically. Board meetings are where key product, pricing and packaging decisions are made. Some of these decisions affect how the core technology is used for example, whether to grant exclusive licenses to governments, set up foreign subsidiaries or get personal security clearances. This is why board members need their own framework for responsibly investing in AI.

The first step in taking responsibility is knowing what on earth is going on. Its easy for startup investors to shrug off the need to know whats going on inside AI-based models. Testing the code to see if it works before sending it off to a customer site is sufficient for many software investors.

However, AI-first products constantly adapt, evolve and spawn new data. Some consider monitoring AI so hard as to be basically impossible. However, we can set up both metrics and management systems to monitor the effects of AI-first products.

We can use hard metrics to figure out if a startups AI-based system is working at all or if its getting out of control. The right metrics to use depend on the type of modeling technique, the data used to train the model and the intended effect of using the prediction. For example, when the goal is hitting a target, one can measure true/false positive/negative rates.

Sensitivity and specificity may also be useful in healthcare applications to get some clues as to the efficacy of a diagnostic product: Does it detect enough diseases enough of the time to warrant the cost and pain of the diagnostic process? The book has an explanation of these metrics and a list of metrics to consider putting in place.

We can also implement a machine learning management loop that catches models before they drift away from reality. Drift is when the model is trained on data that is different from the currently observed data and is measured by comparing the distributions of those two data sets. Measuring model drift regularly is imperative, given that the world changes gradually, suddenly and often.

We can measure gradual changes only if we receive metrics over time, sudden changes can be measured only if we get metrics close to real time, and regular changes are measurable only if we accumulate metrics at the same intervals. The following schematic shows some of the steps involved in a machine learning management loop so that we can realize that its important to constantly and consistently measure the same things at every step of the process of building, testing, deploying and using models.

The issue of bias in AI is a problem both ethical and technical. We deal with the technical part here and summarize management of machine bias by treating it in the same way we often manage human bias: With hard constraints. Setting constraints on what the model can predict, who accesses those predictions, limits on feedback data, acceptable uses of the predictions and more requires effort when designing the system but ensures appropriate alerting.

Additionally, setting standards for training data can increase the likelihood of it considering a wide range of inputs. Speaking to the designer of the model is the best way to reach an understanding of the risks of any bias inherent in their approach. Consider automatic actions such as shutting down or alerting after setting these constraints.

Helping powerful institutions by giving them powerful tools is often interpreted as direct support of the political parties that put them into power. Alignment is often assumed rightly or wrongly and carries consequences. Team members, customers and potential investors aligned with different political parties may not want to work with you. Media may target you. This is to be expected and thus expressed internally as an explicit choice as to whether to work with such institutions.

The primary, most direct political issues arise for investors when companies do work for the military. Weve seen large companies such as Google face employee strikes over the mere potential of taking on military contracts.

Secondary political issues such as personal privacy are more a question of degree in terms of whether they catalyze pressure to limit the use of AI. For example, when civil liberties groups target applications that may encroach on a persons privacy, investors may have to consider restrictions on the use of those applications.

Tertiary political issues are generally industrial, such as how AI may affect the way we work. These are hard for investors to manage, because the impact on society is often unknowable on the timeline over which politicians can operate, i.e., a few years.

Responsible investors will constantly consider all three areas military, privacy and industry of political concern, and set the internal policy-making agenda short, medium and long term according to the proximity of the political risk.

Arguably, AI-first companies that want to bring about peace in our world may take the view that they eventually will have to pick a side to empower. This is a strong point of view to take, but one thats justified by certain (mostly utilitarian) views on violence.

The responsibilities of AI-first investors run deep, and rarely do investors in this field know how deep when theyre just getting started, often failing to fully appreciate the potential impact of their work. Perhaps the solution is to develop a strong ethical framework to consistently apply across all investments.

I havent delved into ethical frameworks because, well, they take tomes to properly consider, a lifetime to construct for oneself and what feels like a lifetime to construct for companies. Suffice to say, my belief is that philosophers could be better utilized in AI-first companies in developing such frameworks.

Until then, investors that are aware of the basics, metrics and politics will hopefully be a good influence on the builders of this most powerful technology.

Disclaimer: The author is an investor in two companies mentioned in this article (HASH and Lilt) through a fund (Zetta), where he is a managing partner.

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The responsibilities of AI-first investors - TechCrunch

The worlds energy systems are changing. Driven by strong demand for clean energy and mounting impacts from climate-driven extreme weather, entities around the world are setting ambitious goals to reduce emissions from the fossil fuels that have powered economic growth for over a century.

The majority of emissions come from three sectors: electricity generation, transportation and buildings. Steep increases in renewables will reduce electric sector emissions and power new loads from transportation and buildings. But the grid must undergo profound changes for this to occur.

Electricity will drive global decarbonisation. The future grid must first be clean. No feasible, affordable path exists to replace gasoline with a carbon-free liquid fuel for vehicles, nor natural gas with a carbon-free alternative for cooking and heating. No path, that is, apart from electrifying vehicles and buildings, which is recognised as the lowest-cost, lowest-risk decarbonisation strategy.

Clean electricity will drive emissions reductions across the economy. Some renewable energy will still come from power plants, but those can be difficult to build, as can be the long transmission lines that bring power to users. By contrast, local renewables can provide clean, affordable power directly to customers more easily, making it decentralised.

The future grid will address key challenges: power outages and economic losses from extreme weather. With these events becoming more frequent and severe, maintaining the grids century-old, centralised architecture is a costly proposition. It must be resilient.

With renewables, growth and variation in electricity services as well as significant unpredictability in supply and demand, the grid must become dynamic. And in order for that grid to function, it must be smart.

Thats where artificial intelligence (AI) comes in.

On an increasingly complex future grid, the number of decisions will far exceed human and conventional digital automation capabilities. Theres already automation on todays grid, but automation can only go so far. Fully enabling a future grid and maximising its benefits will require AI. Ultimately, AI will transform the grid from an aging supplier of commodity electricity to an intelligent system of systems that produces optimised outcomes.

There are three main sectors where AI will drive decarbonisation the most electricity, buildings and transportation.

Leveraging years or decades worth of data, AI will generate forecasts for key factors including weather, renewable energy generation, customer demand and market prices. These forecasts, and learning from predicted vs. actual outcomes, will enable AI to optimise every resource on the grid for every moment of the day. And its real-time control capabilities will execute on forecasts and correct for anomalies, ultimately down to the sub-second level.

More importantly, the role of buildings on a decarbonised grid will change: instead of a passive, predictable consumer of electricity, buildings will become an integrated, dynamic resource and active market participant. Just as important as the selling of clean, locally generated electricity into markets will be the selling of grid services, with buildings flexible loads helping to maintain a balanced, reliable grid. AI-enabled buildings will allow users to match consumption with on-site and off-site renewable generation to achieve 24/7 clean energy objectives.

Storage will be as important as renewables and AI in achieving global decarbonisation, solving the challenge of intermittent renewable generation so that clean energy is available when its needed. Storage will enable buildings and transportation to act as fully flexible grid resources, making up for shortfalls in on-site generation and providing grid services when devices cant.

Building a massive grid that instantaneously balances supply and demand while providing power has been called the greatest engineering feat of the 20th century. Powering a fully decarbonised economy with AI-driven renewables and energy storage may prove to be the greatest achievement of the 21st.

Todays markets bear very little resemblance to those that will underpin a decarbonised grid. The range and value of AI-enabled energy services havent been contemplated in many jurisdictions, let alone the means of incorporating and compensating them in real time. Electricity markets must transform completely.

Every barrier that prevents a customer from buying or selling the clean energy services they want, and that the grid needs, at any time, must be removed. In its place must be efficient, transparent market mechanisms which AI will animate, allowing customers and utilities to realise their desired outcomes with or without human involvement.

Its an annual meeting featuring top examples of public-private cooperation and Fourth Industrial Revolution technologies being used to develop the sustainable development agenda.

It runs alongside the United Nations General Assembly, which this year features a one-day climate summit. This is timely given rising public fears and citizen action over weather conditions, pollution, ocean health and dwindling wildlife. It also reflects the understanding of the growing business case for action.

The UNs Strategic Development Goals and the Paris Agreement provide the architecture for resolving many of these challenges. But to achieve this, we need to change the patterns of production, operation and consumption.

The World Economic Forums work is key, with the summit offering the opportunity to debate, discuss and engage on these issues at a global policy level.

Since policy drives markets, policymakers must instigate needed changes over the next decade to enable full electric sector decarbonisation by 2035, a pillar of many long-term emissions reduction strategies.

AI technology itself must continue to evolve. But the biggest barrier isnt technical its regulatory. Action towards mid-century decarbonisation must occur. The costs and risks of inaction increase every moment and stand between us and the much-needed decarbonised grid of the future.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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AI is transforming the grid. Here's how. - World Economic Forum

SUNNYVALE, Calif., Sept. 15, 2021 /PRNewswire/ -- Plug and Play the largest global innovation platform in the field of Artificial Intelligence, and international accelerator and investment platform INTEMA (Intelligent Machines) launched by AI subsidiary of MTS, largest telecom operator in Eastern Europe (NYSE: MBT, MOEX: MTSS), have commenced a partnership that will see INTEMA and Plug and Play's global ecosystem development, leveraging the platform to support its comprehensive digital transformation strategy in the field of Artificial Intelligence.

Plug and Play Logo (PRNewsfoto/Plug and Play)

The collaboration with Plug and Play will allow INTEMA by MTS AI to expand the number of global AI startups to be included in the accelerator program which provides access to international expertise in Artificial Intelligence and possible investment opportunities.

Plug and Play, headquartered in Silicon Valley, is renowned for connecting startups, corporations, and investors throughout the world to help them to collaborate and scale together. Plug and Play's global ecosystem across the United States, Frankfurt, Amsterdam, Milan, Paris, Abu Dhabi, Barcelona, Shanghai, Singapore, and Tokyo encompasses over 40,000 startups and 500 official corporate partners. In 2020 alone, 2,065 projects were accelerated in Plug and Play.

INTEMA by MTS AI accelerator aims to select best global startups and coach the teams in key areas including technology, product, marketing and sales expertise. As part of the partnership, Plug and Play experts will become mentors in INTEMA acceleration program and share their experience with the participants in the training program to enhance their knowledge, skills and capabilities in order to help them scale their businesses and foster the creation of cutting-edge AI solutions. INTEMA by MTS AI accelerator also welcomes AI experts from leading technology companies such as Samsung, Shazam, Apple, Intel, Nvidia and others to work with its accelerator startups and provide mentorship.

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Each accelerator program participant will receive this expert guidance alongside up to $100,000.00 in early-stage funding from INTEMA. They may also be included in INTEMA fund pipeline which recently made its first investment of $10 million in San Diego-based startup Kneron that makes AI chips (among its investors are Foxconn, Qualcomm, Sequoia and Horizon ventures).

"In the realm of artificial intelligence, we are building a full-cycle system for dealing with ideas, projects, and products. The collaboration with Plug and Play will strengthen our efforts in this process: we seek for potential teams all around the world, support AI solution developers, and take their products to the next level. Going forward, we plan to continue investing in promising AI-focused teams and technologies as part of our venture fund, corporate accelerator, and venture studio," said Alexander Khanin, Head of the MTS AI Center.

"We see soaring demand from investors for products and startups in AI. Experts claim AI to be a major game changer in the upcoming years. We select the best AI teams from all over the world to boost them with our expertise and resources. Our mentors and experts work with leading global tech companies, our team has a solid background investing in tech companies. We give our portfolio companies computing powers of our top tier superpod for free. The mission of our accelerator backed by AI focused VC fund is to give companies and startups an elevator ride so they could disrupt industries with their tech. We are sure that Plug & Play is a great partner for that," - said Alexey Posternak, Chief Financial and Investment Officer at MTS AI Center.

"Serving one of the leading AI global platforms is a great honor for Plug and Play. We are excited to explore the Russian telecommunications market as well with an in-depth approach to be able to drive innovation forward. At Plug and Play we are convinced that established corporations that connect and cooperate with best-in-class startups prosper under the possibilities these collaborations offer to them. The partnership with INTEMA by MTS AI promises to be of great value for both sides when it comes to creating growth opportunities, exploring new markets and accelerating innovation measures," said Saeed Amidi, CEO Plug and Play

"We're thrilled to work with INTEMA by MTS AI platform and expand the possibilities for our startups to do business and expand in new countries. INTEMA by MTS AI will be a key factor to our sourcing efforts and we are aligned internally to engage with startups from a business development and investment capacity. This will better serve MTS AI clients and help them achieve growth," said Alfredo Gomez, Partnerships Manager at Plug and Play.

About INTEMA by MTS AIINTEMA by MTS AI is a worldwide platform for the development, acceleration, and commercialization of AI solutions owned by AI subsidiary of PJSC MTS (NYSE: MBT, MOEX: MTSS). MTS AI is working on a variety of solutions based on computer vision technologies, including a natural language recognition and speech synthesis system, as well as a platform for creation voice robots and smart assistants.INTEMA by MTS AI is a startup accelerator that focuses on AI-based projects with a solid idea, solution prototype, and development. INTEMA r Venture Fund seeks late-seeding A and B series companies in the worldwide market with strong potential for further expansion in the B2B and B2C categories.For more information, visit https://aicenter.mts.ru

About Plug and PlayPlug and Play is the leading innovation platform, connecting startups, corporations, venture capital firms, universities, and government agencies. Headquartered in Silicon Valley, we're present in 35+ locations across five continents. We offer corporate innovation programs and help our corporate partners in every stage of their innovation journey, from education to execution. We also organize startup acceleration programs and have built an in-house VC to drive innovation across multiple industries where we've invested in hundreds of successful companies including Dropbox, Guardant Health, Honey, Lending Club, N26, PayPal, and Rappi. For more information, visit https://www.plugandplaytechcenter.com/

Notes To Editors:Founders of any projects in the field of artificial intelligence can apply for participation in the MTS AI acceleration program. Applications are accepted until October.

Tancredi Intelligent Communication Helen Humphrey T: +442034342321 M: +447449226720 helen@tancredigroup.com

Salamander Davoudi T: +442034342334 M: +447957549906 salamander@tancredigroup.com

Julia UsenkoPR manager MTS AIPhone: +7 (912) 8257112E-mail: ymusenko@mts.ru

Alfredo Gomez SoriaCorporate Partnerships ManagerPhone: +49 (0) 15157674901E-mail: alfredo@pnptc.com

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Plug and Play to collaborate on innovation in Artificial Intelligence with INTEMA by MTS AI - Yahoo Finance

Statista reveals that the AI implementation plans in organizations in the U.S. in 2021 show that only 33% of U.S organizations have started implementing AI in their workplace.

Many people still associate artificial intelligence with science fiction dystopias, but as artificial intelligence advances and becomes more normal in our daily lives, this perception is fading. Artificial intelligence is now a well-known term.

One example is The Really Useful Information Company (TRUiC). According to a news release, they now use AI to power up new startup registrations, domain name selection, and a range of things that traditional brainstorming used to do among humans. For example, if you want to start a new LLC in Texas to tap into the same tax incentives as Elon Musk well, you can do it without the need to even pick up the phone. This is because TRUiC will take you through the process of reviewing the best providers, making a new logo for your business, picking an AI website builder, and picking a domain name or business idea all driven by AI.

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Before we can discuss the impact of AI in the workplace, we first need to understand exactly what artificial intelligence is:

Artificial intelligence (AI) is a broad term that refers to any type of computer software that performs human-like tasks such as learning, planning, and problem-solving.

While the mainstream adoption of artificial intelligence is a new occurrence, it is not a new notion. The contemporary area of artificial intelligence was founded in 1956, but major progress toward establishing an artificial intelligence system and making it a technological reality took decades of effort.

AI can perform jobs with significantly better precision and speed than humans, from automating various areas of the business to forecasting trends in data, revolutionizing not only what organizations and managers can do, but also how they do it. Chatbots, intelligent voice assistants, and conversational AI are revolutionizing the customer experience in healthcare, finance, retail, and travel. Meanwhile, a slew of new machine learning applications is allowing businesses to conduct in-depth analyses of critical internal data in order to revamp their operations.

Whether you are updating your business structure or busy with new business formation, incorporating AI into your daily operation will contribute to the future of your business.

Below are four of the top ways businesses are currently implementing AI in the workplace:

Chatbots are quickly gaining traction in the current digital workplace due to their capacity to decipher spoken and written communications utilizing natural-language processing. Chatbots improve client engagement, reduce mistake rates, and allow employees to respond more quickly.

Many businesses now use them to improve customer service, but they may soon become an indispensable tool for employees as well. Employees might use chatbots to manage reminders, prioritize action items, and seek assistance with routine IT chores like submitting service requests or resetting passwords.

Augmented reality (AR) bridges the gap between the virtual and physical worlds by enhancing interactions with digital content. AR overlays text, icons, and images over the users vision, allowing them access to the information they need to complete a task effectively. Employees may, for example, be in a virtual meeting while zooming in on a floor plan that shows in front of them. Augmented reality could be even more potent in the workplace when paired with AI. Artificial intelligence might add a new layer to virtual and augmented, allowing designers to build more personalized experiences based on data about consumers interests.

According to the US Department of Energy, commercial buildings waste up to 30% of the energy they consume. Many building owners have energy management programs or systems in place to track usage, but the advice they give is frequently based on historical data rather than real-time information. Energy management platforms are becoming smarter as a result of IoT sensors and AI.

For example, AI could assist building owners in predicting peak energy demand. It can detect variations in temperature and humidity as a result of factors such as weather and office density, making the environment more comfortable for everyone.

In the workplace, AI technology aids in the creation of ideal conditions for the development of new products and services. Employees have more free time to interact with consumers and gather vital feedback now that AI tools have taken over the monotonous tasks. They will be able to generate more unique ideas to help the sector expand as a result of this. These changes may lead to increased job satisfaction.

Artificial intelligence will soon be able to do repetitive or difficult problem-solving activities. In industries, AI-enabled computers will make choices in place of humans. With AI being used for customer service and financial choices, it makes sense to incorporate it into the everyday workings of a business.

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Technology Perspective: How AI Helps With New Business Formation - Android Headlines