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The average U.S. high school class has 30 students, according to research from theNational Council on Teacher Quality, and while each student learns in their own way, practice and specific feedback are repeatedly shown to be effective in modern classrooms. With interactive tools likepractice sets, students can receive one-to-one feedback and support without ever leaving an assignment. This saves the educators time, while also providing insight into students learning processes and patterns.

Achieving both aims at once sounds like a tall order, but adaptive learning technologies helpto do just that. Adaptive learning, a model where students are given customized resources and activities to support their unique learning needs, has been around for decades. However, applying advancing artificial intelligence technology opens up a new set of possibilities to transform the future of school into a personal learning experience.

Google for Educationrecently expanded its suite of adaptive learning tools using artificial intelligence, machine learning and user-friendly design to bring robust capabilities into the classroom.

For educators, adaptive learning technologies help boost instruction, reduce administrative burdens and deliver actionable insights into students progress. More time for planning and catch-up work would help alleviate teachers stress, according to anEdWeek Research Center survey.

For students, adaptive learning tech can deepen comprehension of instructional concepts and help them achieve their personal potential. Through interactive lessons and assignments, real-time feedback and just-in-time support, students can advance through lessons in ways that help increase the likelihood of success.

LEARN MORE:Discover how Google for Education supports students and teachers with CDWG.

When a student grasps a new concept, it can create a magical moment where they suddenly get it, says Shantanu Sinha, vice president and general manager of Google for Education. Ensuring that students get access to the right content or material at the right time is a critical part of making this happen.

By prioritizing students individual learning needs and adapting instruction accordingly, personal learning delivers various benefits, from a well-rounded learning experience to increased productivity, according toeducational publisher Pearson.

Practice setsoffer immediate, personal feedback, which is one of the best ways to keep students engaged. When students are on the right track, fast feedback helps them build confidence and celebrate small wins. When students struggle, real-time feedback helps to ensure they truly understand the material before advancing through a lesson.

Making these experiences interactive can dramatically improve the feedback loop for the student, says Sinha. The ability to see their progress and accuracy when working on an assignment, as well as helpful additional content, can guide students and help them learn.

For instance, Google for Education practice sets use AI to deliver encouragement and support the moment students need them. This includes hints, pop-up messages, video lessons and other resources.

Click the bannerbelow to find resources from CDW to digitally transform your classroom.

With practice sets, teachers can build interactive assignments from existing content, and the software automatically customizes support for students. Practice sets also grade assignments automatically, with the AI recognizing equivalent answers and identifying where students go off track. All these capabilities help teachers extend their reach and maximize their time.

Practice sets also leverage AI to provide an overview of class performance and indicate trends. If several students are having trouble with a concept, teachers can see patterns and adjust quickly without manually sorting through students results.

AI-driven technology opens new opportunities for flexible teaching and learning options. OnChromebooks, for instance, teachers can use Screencast to record video lessons. AI transcribes the spoken lessons into text, allowing students to translate those transcripts into dozens of languages.

Googles adaptive learning tools have built-in, best-in-class security and privacy to protect students and educators personal information. Transparency, multilayered safeguards and continuous updates to ensure compliance with new legislation and best practices are central to delivering adaptive instruction that is secure.

Educators can see and manage security settings on Chromebooks andGoogle Workspacefor Education. IT administrators have visibility via Google for Educations Admin Console.

LEARN MORE:How can a Google Workspace for Education audit benefit your K12 district?

Screencast onChrome OSand practice sets inGoogle Classroomare Googles newest offerings in adaptive learning. Other useful tools include:

As adaptive learning technology continues to evolve, it has the potential to transform the learning experience and help teachers better meet students where they are in the learning journey. When the right technology is applied to teaching and learning, teachers and students can go further, faster.

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Google's Adaptive Learning Technologies Help Amplify Educators' Instruction - EdTech Magazine: Focus on K-12

Mon, Aug 22nd 2022 11:20 am

New book co-written by UB philosopher claims AI will never rule the world

AI that would match the general intelligence of humans is impossible, says SUNY Distinguished Professor Barry Smith

By the University at Buffalo

Elon Musk in 2020 said that artificial intelligence (AI) within five years would surpass human intelligence on its way to becoming an immortal dictator over humanity. But a new book co-written by a University at Buffalo philosophy professor argues that wont happen not by 2025, not ever!

Barry Smith, Ph.D., SUNY Distinguished Professor in the department of philosophy in UBs College of Arts and Sciences, and Jobst Landgrebe, Ph.D., founder of Cognotekt, a German AI company, have co-authored Why Machines Will Never Rule the World: Artificial Intelligence without Fear.

Their book presents a powerful argument against the possibility of engineering machines that can surpass human intelligence. Machine learning and all other working software applications the proud accomplishments of those involved in AI research are for Smith and Landgrebe far from anything resembling the capacity of humans. Further, they argue that any incremental progress thats unfolding in the field of AI research will in practical terms bring it no closer to the full functioning possibility of the human brain.

Smith and Landgrebe offer a critical examination of AIs unjustifiable projections, such as machines detaching themselves from humanity, self-replicating, and becoming full ethical agents. There cannot be a machine will, they say. Every single AI application rests on the intentions of human beings including intentions to produce random outputs. This means the Singularity, a point when AI becomes uncontrollable and irreversible (like a Skynet moment from the Terminator movie franchise) is not going to occur. Wild claims to the contrary serve only to inflate AIs potential and distort public understanding of the technologys nature, possibilities and limits.

Reaching across the borders of several scientific disciplines, Smith and Landgrebe argue that the idea of a general artificial intelligence (AGI) the ability of computers to emulate and go beyond the general intelligence of humans rests on fundamental mathematical impossibilities that are analogous in physics to the impossibility of building a perpetual motion machine. AI that would match the general intelligence of humans is impossible because of the mathematical limits on what can be modeled and is computable. These limits are accepted by practically everyone working in the field; yet they have thus far failed to appreciate their consequences for what an AI can achieve.

To overcome these barriers would require a revolution in mathematics that would be of greater significance than the invention of the calculus by Newton and Leibniz more than 350 years ago, says Smith, one of the worlds most cited contemporary philosophers. We are not holding our breath.

Landgrebe points out that, As can be verified by talking to mathematicians and physicists working at the limits of their respective disciplines, there is nothing even on the horizon which would suggest that a revolution of this sort might one day be achievable. Mathematics cannot fully model the behaviors of complex systems like the human organism, he says.

AI has many highly impressive success stories, and considerable funding has been dedicated toward advancing its frontier beyond the achievements in narrow, well-defined fields such as text translation and image recognition. Much of the investment to push the technology forward into areas requiring the machine counterpart of general intelligence may, the authors say, be money down the drain.

The text generator GPT-3 has shown itself capable of producing different sorts of convincing outputs across many divergent fields, Smith says. Unfortunately, its users soon recognize that mixed in with these outputs there are also embarrassing errors, so that the convincing outputs themselves began to appear as nothing more than clever parlor tricks.

AIs role in sequencing the human genome led to suggestions for how it might help find cures for many human diseases; yet, after 20 years of additional research (in which both Smith and Landgrebe have participated), little has been produced to support optimism of this sort.

In certain completely rule-determined confined settings, machine learning can be used to create algorithms that outperform humans, Smith says. But this does not mean that they can discover the rules governing just any activity taking place in an open environment, which is what the human brain achieves every day.

Technology skeptics do not, of course, have a perfect record. Theyve been wrong in regard to breakthroughs ranging from space flight to nanotechnology. But Smith and Landgrebe say their arguments are based on the mathematical implications of the theory of complex systems. For mathematical reasons, AI cannot mimic the way the human brain functions. In fact, the authors say that its impossible to engineer a machine that would rival the cognitive performance of a crow.

An AGI is impossible, says Smith. As our book shows, there can be no general artificial intelligence because it is beyond the boundary of what is even in principle achievable by means of a machine.

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Terminator? Skynet? No way. Machines will never rule the world, according to book by UB philosopher - Niagara Frontier Publications

Advanced computer programs influence, and can even dictate, meaningful parts of our lives. Think of streaming services, credit scores, facial recognition software.

As this technology becomes more sophisticated and more pervasive, its important to understand the basic terminology.

People often use algorithm, machine learning and artificial intelligence interchangeably. There is some overlap, but theyre not the same things.

We decided to call up a few experts to help us get a firm grasp on these concepts, starting with a basic definition of algorithm. The following is an edited transcript of the episode.

Melanie Mitchell, Davis professor of complexity at the Santa Fe Institute, offered a simple explanation of a computer algorithm.

An algorithm is a set of steps for solving a problem or accomplishing a goal, she said.

The next step up is machine learning, which uses algorithms.

Rather than a person programming in the rules, the system itself has learned, Mitchell said.

For example, speech recognition software, which uses data to learn which sounds combine to become words and sentences. And this kind of machine learning is a key component of artificial intelligence.

Artificial intelligence is basically capabilities of computers to mimic human cognitive functions, said Anjana Susarla, who teaches responsible AI at Michigan State Universitys Broad College of Business.

She said we should think of AI as an umbrella term.

AI is much more broader, all-encompassing, compared to only machine learning or algorithms, Susarla said.

Thats why you might hear AI as a loose description for a range of things that show some level of intelligence. Like software that examines the photos on your phone to sort out the ones with cats to advanced spelunking robots that explore caves.

Heres another way to think of the differences among these tools: cooking.

Bethany Edmunds, professor and director of computing programs at Northeastern University, compares it to cooking.

She says an algorithm is basically a recipe step-by-step instructions on how to prepare something to solve the problem of being hungry.

If you took the machine learning approach, you would show a computer the ingredients you have and what you want for the end result. Lets say, a cake.

So maybe it would take every combination of every type of food and put them all together to try and replicate the cake that was provided for it, she said.

AI would turn the whole problem of being hungry over to the computer program, determining or even buying ingredients, choosing a recipe or creating a new one. Just like a human would.

So why do these distinctions matter? Well, for one thing, these tools sometimes produce results with biased outcomes.

Its really important to be able to articulate what those concerns are, Edmunds said. So that you can really dissect where the problem is and how we go about solving it.

Because algorithms, machine learning and AI are pretty much baked into our lives at this point.

Columbia Universitys engineering school has a further explanation of artificial intelligence and machine learning, and it lists other tools besides machine learning that can be part of AI. Like deep learning, neural networks, computer vision and natural language processing.

Over at the Massachusetts Institute of Technology, they point out that machine learning and AI are often used interchangeably because these days, most AI includes some amount of machine learning. A piece from MITs Sloan School of Management also gets into the different subcategories of machine learning. Supervised, unsupervised and reinforcement, like trial and error with kind of digital rewards. For example, teaching an autonomous vehicle to drive by letting the system know when it made the right decision like not hitting a pedestrian, for instance.

That piece also points to a 2020 survey from Deloitte, which found that 67% of companies are already using machine learning, and 97% were planning to in the future.

IBM has a helpful graphic to explain the relationship among AI, machine learning, neural networks and deep learning, presenting them as Russian nesting dolls with the broad category of AI as the biggest one.

And finally, with so many businesses using these tools, the Federal Trade Commission has a blog laying out some of the consumer risks associated with AI and the agencys expectations of how companies should deploy it.

Read more:
The ABCs of AI, algorithms and machine learning - Marketplace

Innovative scientific and technological developments have ushered in a remarkable transformation in medicine that continues to impact virtually all stakeholders from patients to providers to Healthcare Organizations (HCOs) and the community in general.1,2 Increasingly incorporated into clinical practice over the past few decades, these innovations include widespread use of Electronic Health Records (EHR), telemedicine, robotics, and decision support for surgical procedures. Ingestible microchips allow healthcare providers to monitor patient compliance with prescribed pharmacotherapies and their therapeutic efficacy through big data analysis,15 as well as streamlining drug design, screening, and discovery.6 Adoption of novel medical technologies has allowed US healthcare to maintain its vanguard position in select domains of clinical care such as improving access by reducing wait times, enriching patient-provider communication, enhancing diagnostic accuracy, improving patient satisfaction, augmenting outcome prediction, decreasing mortality, and extending life expectancy.35,7

Yet despite the theoretical advantages of these innovative medical technologies, many issues remain requiring careful consideration as we integrate these novel technologies into our armamentarium. This descriptive literature-based article explicates on the advantages, future potential, challenges, and caveats with the predictable and impending importation of AI and ML into all facets of healthcare.

By far the most revolutionary of these novel technologies is Artificial Intelligence (AI), a branch of computer science that attempts to construct intelligent entities via Machine Learning (ML), which is the ability of computers to learn without being explicitly programed.8 ML utilizes algorithms to identify patterns, and its subspecialty Deep Learning (DL) employs artificial neural networks with intervening frameworks to identify patterns and data.1,8 Although ML was first conceived by computer scientist Arthur Samuel as far back as 1956, applications of AI have only recently begun to pervade our daily life with computers simulating human cognitioneg, visual perception, speech recognition, decision-making, and language translation.8 Everyday examples of AI include smart phones, autonomous vehicles, digital assistants (eg, Siri, Alexa), chatbots and auto-correcting software, online banking, facial recognition, and transportation (eg, Uber, air traffic control operations, etc.). The iterative nature of ML allows the machine to adapt its systems and outputs following exposure to new data with supervised learningie, utilizing training algorithms to predict future events from historical data inputsor unsupervised learning, whereby the machine explores the data and attempts to develop patterns or structures de novo. The latter methodology is often used to determine and distinguish outliers. Neural networks in AI utilize an adaptive system comprised of an interconnected group of artificial neurons and mathematical or computational modeling for processing information from input and output data via pattern recognition.9 Through predictive analytics, ML has demonstrated its effectiveness in the realm of finance (eg, identifying credit card fraud) and in the retail industry to anticipate customer behavior.1,10,11

Extrapolation of AI to medicine and healthcare is expected to increase exponentially in the three principal domains of research, teaching, and clinical care. With improved computational efficiencies, common applications of ML in healthcare will include enhanced diagnostic modalities, improved therapeutic interventions, augmenting and refining workflow by processing large amounts of hospital and national EHR data, more accurate clinical course and prediction through precision and personalized medicine, and genome interpretation. ML can provide basic clinical triage in geographical areas inaccessible to specialty care. It can also detect treatable psychiatric conditions via analysis of affective and anxiety disorders using speech patterns and facial expressions (eg, bipolar disorder, major depression, anxiety spectrum and psychotic disorders, attention deficit hyperactivity disorder, addiction disorders, Tourettes Syndrome, etc.)12,13 (Figure 1). Deep learning algorithms are highly effective compared to human interpretation in medical subspecialties where pattern recognition plays a dominant role, such as dermatology, hematology, oncology, histopathology, ophthalmology, radiology (eg, programmed image analyses), and neurology (eg, analysis for seizures utilizing electroencephalography). Artificial neural networks are being developed and employed for diagnostic accuracy, timely interventions, outcomes and prognostication of neurosurgical conditions, such as spinal stenosis, traumatic brain injury, brain tumors, and cerebral vasospasm following aneurysmal subarachnoid hemorrhage.14 Theoretically, ML can improve triage by directing patients to proper treatments at lower cost and by keeping those with chronic conditions out of costly and time-intensive emergency care centers. In clinical practice, ~5% of all patients account for 50% of healthcare costs, and those with chronic medical conditions comprise 85% of total US healthcare costs.3

Figure 1 Potential Applications of Machine Learning.

Patients can benefit from ML in other ways. For follow-up visits, not having to arrange transportation or take time off work for face-to-face interaction with healthcare providers may be an attractive alternative to patients and to the community, even more so in restricted circumstances like the recent COVID-19 pandemic-associated lockdowns and social distancing.

Ongoing ML-related research and its applications are robust. Companies developing automation, topological data analysis, genetic mapping, and communications systems include Pathway Genomics, Digital Reasoning Systems, Ayandi, Apixio, Butterfly Network, Benevolent AI, Flatiron Health, and several others.1,10

Despite the many theoretical advantages and potential benefits of ML in healthcare, several challenges (Figure 2) must be met15 before it can achieve broader acceptance and application.

Figure 2 Caveats and Challenges with use of Machine Learning.

Frequent software updates will be necessary to ensure continued improvement in ML-assisted models over time. Encouraging the use of such software, the Food and Drug Administration has recommended a pre-certified approach for agility.1,2 To be of pragmatic clinical import, high-quality input-data is paramount for validating and refining diagnostic and therapeutic procedures. At present, however, there is a dearth of robust comparative data that can be validated against the commonly accepted gold standard, comprised of blinded, placebo-controlled randomized clinical trials versus the ML-output data that is typically an area-under-the-curve analysis.1,7 Clinical data generated from ML-assisted calculations and more rigorous multi-variate analysis will entail integration with other relevant patient demographic information (eg, socio-economic status, including values, social and cultural norms, faith and belief systems, social support structures in-situ, etc.).16

All stakeholders in the healthcare delivery system (HCOs, providers, patients, and the community) will have to adjust to the paradigm shift away from traditional in-person interactions. Healthcare providers will have to surmount actual or perceived added workload to avoid burnout especially during the initial adaptive phase. They will also have to cope with increased ML-generated false-positive and -negative alerts. The traditional practice of clinical medicine is deeply entrenched in the framework of formulating a clinical hypothesis via rigorous history-taking and physical examination followed by sequential confirmation through judicious ancillary and diagnostic testing. Such traditional in-person interactions have underscored the importance of an empathetic approach to the provider-patient relationship. This traditional view has been characterized as archaic, particularly by those with a futuristic mindset, who envision an evolutionary change leading to whole body scans that deliver a more accurate assessment of health and diagnosis of disease. However, incidental findings not attributable to symptoms may lead to excessive ancillary tests underscoring the adage testing begets more testing.17

Healthcare is one of the fastest growing segments of the world economy and is presently at a crossroads of unprecedented transformation. As an example, US healthcare expenditure has accelerated dramatically over the past several decades (~19% of Gross National Product; exceeding $4.1 trillion, or $12,500 per person per year)18 with widespread ramifications for all stakeholders including patients and their families, healthcare providers, government, community, and the US economy.1,35 A paradigm shift from volume-based to performance-based reimbursements from third-party payers warrants focus on some of the most urgent issues in healthcare including cost containment, access, and providing low-cost, high-value healthcare commensurate with the proposed six-domain framework (safe, effective, patient-centered, timely, efficient, and equitable) articulated by the Institute of Medicine in 2001.35,19 Of note, uncontrolled use of expensive technology and excessive ancillary testing account for ~2530% of total healthcare costs.17 While technologies will probably never completely replace the function of healthcare providers, they will definitely transform healthcare, benefiting both providers and patients. However, there is a paucity of costbenefit data and analysis of the use of these innovative emerging medical technologies. All stakeholders should remain cost-conscious as the newer technological diagnostic approaches may further drive up the already rising costs of healthcare. Educating and training the next generation of healthcare providers in the context of AI will also require transformation with simulation approaches and inter-professional education. Therefore, the value proposition of novel technologies must be critically appraised via longitudinal and continuous valuations and patient outcomes in terms of its impact on health and disease management.13 To mitigate healthcare costs, we must control the technological imperativethe overuse of technology because of easy availability without due consideration to disease course or outcomes and irrespective of costbenefit ratio.3

Issues surrounding consumer privacy and proprietorship of colossal quantities of healthcare data under an AI regime are legitimate concerns. Malicious or unintentional breaches may result in financial or other harm. Akin to the challenges encountered with EHR, easy access to data and interoperability with broader compatibility of interfaces by healthcare providers spread across space and time will present unique challenges. Databases will likely be owned by large profit-oriented technology companies who may decide to dispense data to third parties. Additional costs are predictable as well, particularly during the early stages of development of ML algorithms, which is likely to be more bearable to large HCOs. Delay in the use of such processes is anticipated by smaller organizations with resulting potential for mergers and acquisitions or even failure of smaller hospitals and clinics. Concerns regarding ownership, responsibility, and accountability of ML algorithms may arise owing to the probability of detrimental outcomes, which ideally should be apportioned between developer, interpreter, healthcare provider, and patient.1 Simulation techniques can be preemptively utilized for ML training for clinical scenarios; practice runs may require formal certification courses and workshops. Regulations must be developed by policymakers and legislative bodies to delineate the role of third-party payers in ML-assisted healthcare financing. Finally, education and training via media outlets, internet, and social media will be necessary to address public opinion, misperceptions, and nave expectations about ML-assisted algorithms.7

For centuries, the practice of medicine has been deeply embedded in a tradition of meticulous history-taking, physical examination, and thoughtful ancillary investigations to confirm clinical hypotheses and diagnoses. The great physician, Sir William Osler (18491919)14,20 encapsulated the desired practice of good medicine with his famous quotes, Listen to your patient he is telling you the diagnosis, The good physician treats the disease; the great physician treats the patient who has the disease, and Medicine is a science of uncertainty and an art of probability. With rapid technological advances, we are at the crossroads of practicing medicine that would be distinctly different from the traditional approach and practice(s), a change that may be characterized as evolutionary.

AI and ML have enormous potential to transform healthcare and the practice of medicine, although these modalities will never substitute an astute and empathetic bedside clinician. Furthermore, several issues remain as to whether their value proposition and cost-benefit are complementary to the overarching focus on providing low-cost, high-value healthcare to the community at large. While innovative technological advances play a critical role in the rapid diagnosis and management of disease, the phenomenon of the technological imperative35,17 deserves special consideration among both public and providers for the future use of AI and ML in delivering healthcare.

The author reports no conflicts of interest in this work.

1. Bhardwaj R, Nambiar AR, Dutta D A Study of Machine Learning in Healthcare. 2017 IEEE 41st Annual Computer Software and Applications Conference. 236241. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8029924. Accessed March 30, 2022.

2. Deo RC. Machine Learning in Medicine. Circulation. 2015;132:19201930. doi:10.1161/CIRCULATIONAHA.115.001593

3. Shi L, Singh DA. Delivering Health Care in America: A Systems Approach. 7th ed. Burlington, MA: Jones & Bartlett Learning; 2019.

4. Barr DA. Introduction to US Health Policy. The Organization, Financing, and Delivery of Health Care in America. 4th ed. Baltimore, MD: John Hopkins University Press; 2016.

5. Wilensky SE, Teitelbaum JB. Essentials of Health Policy and Law. Fourth ed. Burlington, MA: Jones & Bartlett Learning; 2020.

6. Gupta R, Srivastava D, Sahu M, Tiwan S, Ambasta RK, Kumar P. Artificial intelligence to deep learning; machine intelligence approach for drug discovery. Mol Divers. 2021;25:13151360. doi:10.1007/s11030-021-10217-3

7. Dabi A, Taylor AJ. Machine Learning, Ethics and Brain Death Concepts and Framework. Arch Neurol Neurol Disord. 2020;3:19.

8. Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Int Med. 2018;284:603619. doi:10.1111/joim.12822

9. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982;79:25542558. doi:10.1073/pnas.79.8.2554

10. Ghassemi M, Naumann T, Schulam P, Beam AL, Ranganath R Opportunities in Machine Learning for Healthcare. 2018. Available from: https://pdfs.semanticscholar.org/1e0b/f0543d2f3def3e34c51bd40abb22a05937bc.pdf. Accessed March 30, 2022.

11. Jnr YA Artificial Intelligence and Healthcare: a Qualitative Review of Recent Advances and Predictions for the Future. Available from: https://pimr.org.in/2019-vol7-issue-3/YawAnsongJnr_v3.pdf. Accessed March 30, 2022.

12. Chandler C, Foltz PW, Elvevag B. Using machine learning in Psychiatry; the need to establish a Framework that nurtures trustworthiness. Schizophr Bull. 2019;46:1114.

13. Ray A, Bhardwaj A, Malik YK, Singh S, Gupta R. Artificial intelligence and Psychiatry: an overview. Asian J Psychiatr. 2022;70:103021. doi:10.1016/j.ajp.2022.103021

14. Ganapathy K Artificial intelligence in neurosciences-are we really there? Available from: https://www.sciencedirect.com/science/article/pii/B9780323900379000084. Accessed June 10, 2022.

15. Sunarti S, Rahman FF, Naufal M, Risky M, Febriyanto K, Mashina R. Artificial intelligence in healthcare: opportunities and risk for future. Gac Sinat. 2012;35(S1):S67S70. doi:10.1016/j.gaceta.2020.12.019.

16. Yu B, Beam A, Kohane I. Artificial Intelligence in Healthcare. Nature Biomed Eng. 2018;2:719731. doi:10.1038/s41551-018-0305-z

17. Bhardwaj A. Excessive Ancillary Testing by Healthcare Providers: reasons and Proposed Solutions. J Hospital Med Management. 2019;5(1):16.

18. Fact Sheet NHE. Centers for Medicare and Medicaid Services. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet. Accessed April 14, 2022.

19. Institute of Medicine (IOM). Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, D.C: National Academy Press; 2001.

20. Bliss M. William Osler: A Life in Medicine. New York, NY: Oxford University Press; 1999.

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Artificial Intelligence and Machine Learning in Healthcare | JHL - Dove Medical Press

Machine learning is a nifty branch of artificial intelligence (AI) that uses algorithms to make predictions. In essence, its giving computers the power to learn by themselves without any human interaction.

While this may initially draw thoughts to out-of-control sentient robots in sci-fi films, youve probably been using the technology every day. From what appears at the top of your social media feed, to the life-saving (or life-ruining) predictive text system in your mobile phone or even the sci-fi flicks that Netflix recommends you after finishing Blade Runner 2049, machine learning has been integrated into mainstream technology for decades. Its now even being used to treat cancer patients and help doctors predict the outcome of treatments.

The sole way of programming computers, before AI, would be to create a specific and detailed set of instructions for them to follow. This is a time-consuming task completed by one person or whole teams of people but sometimes, its just not possible at all.

For example, you could quite easily get a computer to create an artistic replica of your favourite family photo by giving it a precise set of instructions. But it would be extremely difficult to tell a computer how to recognise and identify different people within that photo. This is where machine learning comes into play, programming the computer to learn through experience much like humans would, which is what artificial intelligence is all about.

Most businesses handling large amounts of data have discovered the advantages of using machine learning technology. Its fast becoming an essential for organisations wanting to be at the cutting edge of societal predications or companies looking to beat their competitors to the latest trends and profitable opportunities.

Transport, retail, governments, healthcare, financial services and other sectors are all utilising the technology to gain valuable insights that may not have been attainable through manual action.

The most common and recognisable use of machine learning for businesses are chatbots. Companies have been able to implement this technology to deal with customer queries around the clock without increasing their headcount. Facebook Messenger is a popular platform which allows businesses to easily program a chatbot to perform tasks, understand questions and guide customers through to where they need to go.

Online retail businesses like Amazon, ASOS and eBay use machine learning to recommend their customers products they think theyll be interested in. This is a division of the technology called customer behaviour modelling. Using collected data on their customers habits, companies are able to categorise what users with similar browsing behaviours might want to see.

This trend is set to carry on growing. Data from GlobalData shows the proportion of technology and communications companies hiring for AI related positions in May was up 58.9% from those hiring last year, while recent research from Helomics predicts the global AI market hitting a whopping $20bn by 2025.

GlobalData is the parent company of Verdict and its sister publications.

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What is machine learning and why does it matter for business? - Verdict