The singularity is coming for us, day by creeping day. Artificial intelligence is starting to write about cycling. It is starting to create pictures of cycling. And now, it is starting to predict the results of races that havent even happened yet.

There are humans involved at some point there always are, before the end of everything. In this case, it is a data and analytics consultancy called Decision Inc., Australia. The humans developed the modelling, fed it to their machine learning tool, let it marinate for a bit [that may be creative license] and then, the magic happened.

Machine Learning is a form of Artificial Intelligence which uses advanced data analytics [to] solve complex issues, explained Decision Inc, Australia CEO, Aiden Heke. It uses algorithms to best imitate how humans solve problems or predict outcomes.

Since the technology has evolved so much over the past few decades, we thought: why not use it to predict the outcome of the UCI World Championships?

First up, the womens road race:

A caveatthe Machines were crunching their numbers before Annemiek van Vleuten crashed out of the mixed team time trial, putting her start at risk. Also, apparently The Machines dont rate Grace Brown as a top 10 favourite. But all that aside? Those are certainly some credible names.

To the men:

Again, some curiosities in here for me. The podium seems credible, but I think Van der Poel is a bit more of a dark horse than this is letting on. Pogaar seems low; Almeida seems high. Im also furious about the Juraj Sagan erasure, but that is a me thing, not a you thing, and certainly not an AI thing.

Decision Inc. is likening their cycling foray to Deep Blue, an early machine learning venture from the mid-1990s that famously vanquished chess grandmaster Garry Kasparov. Its why were putting it to the test, to see just how far its come, said Decision Inc. CEO Aiden Heke. Were keen for everyone who fancies themselves as a bit of an expert on cycling to see if they can win where Kasparov couldnt: against the Machine.

If you want to show that you know more about this weekends cycling than a series of computer calculations, you can head to the companys Instagram account where you could win some signed cycling goodies.

Or, you can just wade into the comments here and tell us who your pick is. Thatd be fun too.

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Machine learning has predicted the winners of the Worlds - CyclingTips

image:Example of the macro placement algorithm proposed by Google. view more

Credit: Science China Press

Electronic design automation (EDA) or computer-aided design (CAD) is a category of software tools for designing electronic systems, such as integrated circuits (ICs). By EDA tools, designers can finish the design flow of very large scale integrated (VLSI) chips with billions of transistors. EDA tools are essential to modern VLSI design due to the large scale and high complexity of electronic systems.

Recently, with the boom of artificial intelligence (AI) algorithms, the EDA community are actively exploring AI for IC techniques for the design of advanced chips. Many studies have explored machine learning (ML) based techniques for cross-stage prediction tasks in the design flow to achieve faster design convergence. For example, Google published a paper in Nature in 2021 entitled A graph placement methodology for fast chip design, leveraging reinforcement learning (RL) to place macros in a chip design. The basic idea is to regard the chip layout as a Go board, while each macro as a stone. In this way, an RL agent can be pre-trained with 10,000 internal design samples and learn to place one macro at a time. By finetuning the agent on each design for around 6 hours, it can outperform the performance of conventional EDA tools on Googles TPU chips, and achieve better performance, power, and area (PPA).

It can be seen that AI for EDA is being actively explored in the design automation community. Although building ML models usually requires a large amount of data, most studies can only generate small internal datasets for validation, due to the lack of large public datasets and the difficulty in data generation. To this end, an open-source dataset dedicated to ML tasks in EDA is urgently desired.

To address this issue, the research group from Peking University has released the first open-source dataset, called CircuitNet, which is dedicated to AI for IC applications in VLSI CAD. The dataset consists of over 10K samples and 54 synthesized circuit netlists from six open-source RISC-V designs, provides holistic support for cross-stage prediction tasks, and supports tasks including routing congestion prediction, design rule check (DRC) violation prediction and IR drop prediction. The main characteristics of CircuitNet can be summarized as follows:

To evaluate the effectiveness of CircuitNet, the authors validate the dataset by experiments on three prediction tasks: congestion, DRC violations, and IR drop. Each experiment takes a method from recent studies and evaluates its result on CircuitNet with the same evaluation metrics as the original studies. Overall, the results are consistent with the original publications, which demonstrates the effectiveness of CircuitNet. A detailed tutorial about the experimental setup is available on the webpage (https://circuitnet.github.io/). In the future, the authors plan to incorporate more data samples with large-scale designs in advanced technology nodes to improve the scale and diversity of the dataset.

See the article:

CircuitNet: An Open-Source Dataset for Machine Learning Applications in Electronic Design Automation (EDA)

https://doi.org/10.1007/s11432-022-3571-8

Science China Information Sciences

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Peking University released the first open-source dataset for machine learning applications in fast chip design - EurekAlert

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Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study - BMC...

SMITHFIELD, RI Bryant University announces the launch of four new graduate programs to empower students and working professionals with the knowledge, skills, and advanced credentials to succeed in the global, data-driven digital economy. New STEM-designated in-person Masters degree programs in Business Analytics, Data Science, and Healthcare Informatics are enrolling for Fall of 2023. The Masters in Taxation will be delivered online and is also slated for next fall.

"There is an urgent need for leaders and analysts who can see connections and innovate to develop smart, effective strategies for the way business gets done and problems get solved.

As business needs change and industry boundaries blur, Bryant is committed to developing interdisciplinary academic programs and curricula that incorporate business, analytics, artificial intelligence, machine learning, finance, andhealth sciences to meet workforce demands.

These new programs provide opportunities for undergraduates to create pathways to career-accelerating graduate degrees. Professionals at all levelsfrom early career employees to C-level executiveswill be able to uplevel their skills and advance in their careers. All programs are available to students and professionals around the world, and several programs offer 4+1 options for Bryant students.

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Through Bryants marketplace-driven approach and signature real-world experiential education, the new data-centric graduate programs are answering the call for educated and skilled professionals to perform in key roles in top industries where skilled data scientists and analysts are in high demand, including health sciences, financial services, accounting, digital marketing, cyber security, manufacturing, and energy.

Through Vision 2030, we are forging a new era of growth and academic innovation at Bryant University.

Developing new graduate academic programs aligned to evolving workforce demand is part of a key Bryant University Vision 2030 Strategic Plan.

Through Vision 2030, we are forging a new era of growth and academic innovation at Bryant University, says Bryant University President and respected economistRoss Gittell, Ph.D. The value and return on investment on our innovative, highly ranked academic programs is attracting increasing attention of students, families, alumni, media, and corporate partners around the world.

The return on investment on a Bryant education is in the top 1% nationally, according to a recent survey by theGeorgetown University Center on Education and the Workforce.

Advances in technology, artificial intelligence, and machine learning are increasingly integral parts of life and business today. There is an urgent need for leaders and analysts who can see connections and innovate to develop smart, effective strategies to solve problems, says Provost and Chief Executive Officer Rupendra Paliwal Ph.D. These new graduate programs build on our historic strengths and culture of creativity and innovation to prepare our students to be leaders, disruptors, and valuable contributors anywhere in the world.

Business Analytics, Data Science, and Taxation will join other successful programs offered by the College of Business including the MBA, Professional MBA Online, and Master of Professional Accounting (MPAC). Healthcare Informatics is offered by the newly launched School of Health and Behavioral Sciences joining the Master of Science in Physician Assistant Studies (MSPAS), which launched in 2014. Additional graduate programs offered through the College of Arts and Sciences will be announced this fall.

More About the new programs

The Master of Science in Business Analytics prepares future business leaders to use advanced analytics to support organizational goals and strategies and use analytics to tell compelling stories that impact business strategy. Working with state-of-the-art business analytics tools and techniques, students learn the whole process of data analytics lifecycle from business understanding, data preparation, data exploration, model building, and data visualization and communication. The MSBA is a full-time, in-person cohort program comprising eight required business analytics courses and a three-course specialization, or a generalist track that tailors electives to individual personal and professional needs.

Building on the strengths of Bryant University in business and undergraduate data science programs, MSDS program is applied with a foundation in business and helps train the next generation of data scientists to work in various fields. The programs core courses include data ethics, statistics, machine learning, deep learning, natural language processing, large-scale data analytics and more. The MSDS program is a full-time, in-person cohort program and will run over the fall, spring, and summer sessions. Students will complete eight required data science courses and choose a three-course specialization, or a generalist track that tailors electives to individual personal and professional needs.

Healthcare Informaticsis an interdisciplinary field of study in the healthcare industry that uses information technology to organize and analyze health data and records to improve healthcare outcomes. Bryants program provides a holistic understanding of the healthcare system and emphasizes the need for collaboration to improve healthcare delivery and patient outcomes. Graduates of the program are equipped with knowledge of the healthcare industry and technology solutions and the technical skills needed to effectively analyze complex health data, manage evolving health information systems and support the increased utilization of electronic health records. The 10-course, 30-credit, in person program can be completed in 18 months or 12 months with classes over winter and summer sessions.

Bryants Master of Science in Taxation, offered online, will prepare graduates to enhance or launch their professional careers in accounting with a focus on taxation. Gaining in-depth expertise in taxation will enable graduates to understand the nuances of complex tax-related issues in terms of theory and practical application for individuals, partnerships and corporations. The MST program will incorporate data analytics and visualization with machine learning technology to ensure that graduates are well-equipped to best serve their organizations and clients. Graduates will be prepared to advise on retirement and compensation plans as well as navigate estate planning. The 10-course, 31-credit, online program will be delivered in 10-week increments with specific start times to be announced soon.

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For more information about Bryants Graduate programs, contact the Bryants Graduate Programs office at graduateprograms@bryant.edu or 401-232-6230.

About Bryant University

For 160 years, Bryant University has been at the forefront of delivering an exceptional education that anticipates the future and prepares students to be innovative leaders of character in a changing world. The University delivers a uniquely integrated academic and student life experience with nationally recognized academic programs at the intersection of business, liberal arts, and STEM fields. Located on a beautiful 428-acre campus in Smithfield, R.I., Bryantis recognized as a top 1% national leader in student education outcomes and return on investment and regularly receives high rankings fromU.S. News and World Report, Money, Bloomberg Businessweek, Wall Street Journal, College Factual and Barrons. Visitwww.bryant.edu.

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Bryant launches graduate programs in Business Analytics, Data Science, Healthcare Informatics, and Taxation - Bryant University

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Nearly 20% of pregnancies in people with lupus result in an adverse pregnancy outcome (APO). In a new study, scientists were able to improve prediction accuracy of APOs using machine learning. Machine learning refers to the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.

Using a previously developed APO prediction model utilizing data from a larger multi-center, multi-ethnic study of lupus pregnancies known as the Predictors of pRegnancy Outcome: bioMarkers In Antiphospholid Antibody Syndrome and the Systemic Lupus Erythematosus (PROMISSE) study, and statistical analysis coupled with machine learning, researchers analyzed data from 385 women in their first trimester of pregnancy. They identified lupus anticoagulant positivity, disease assessment score, diastolic blood pressure or resting heartbeat, current use of antihypertension medication, and platelet count as significant baseline predictors of APO.

Researchers suggest that the ability to identify, lupus patients at high risk of APO early in pregnancy, could enhance the capacity to manage these patients and conduct trials of new treatments to prevent pre-eclampsia and placental insufficiency.

Further studies to identify new biomarkers and risk factors for APO are still needed. The Lupus Foundation of America provided the study author, Jane Salmon, MD, with a three-year grant for her IMPACT study, the first trial of a biologic therapy to prevent adverse pregnancy outcomes in high-risk pregnancies in patients with antiphospholipid syndrome (APS) with or without systemic lupus erythematosus (SLE), which also helped support this new research. Learn more about lupus and pregnancy.

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Machine Learning Can Be Used to Improve the Ability to Predict Adverse Pregnancy Outcomes in Women with Lupus - Lupus Foundation of America

By AdExchanger Guest Columnist

Data-Driven Thinking is written by members of the media community and contains fresh ideas on the digital revolution in media.

Todays column is written by Jasmine Jia, associate director of data science at Blockthrough.

The term machine learning seems to have a magical effect as a sales buzzword. Couple that with the term data science, and lots of companies think they have a winning formula for attracting new clients.

Is it smoke and mirrors? Often, the answer is yes.

What is quite real though is the need for best practices in data science and for companies to invest in and fully support talent that can apply those principles effectively.

Laying the foundation for machine learning

Machine learning success starts with hiring talent that can harness machine learning a team of skilled data scientists which is very expensive. Adding to the cost is time. It takes a lot of it to build a data science team and integrate them with other teams across operations.

A successful machine learning pipeline requires data cleaning, data exploration, feature extraction, model building, model validation and more. You also need to keep maintaining and evolving that pipeline. And not only is the cost high, but companies also rarely have the patience and time to manage this process and still meet their ROI objectives.

Defining best practices

With the right talent and pipeline in place, the next step is establishing best practices. This is vital. Machine learning depends on how you implement it, what problem you use it to solve, and how you deeply integrate it with your company.

To paint a picture of how things can go wrong just think about the times that imbalanced data sets led to what the media called racist robots and automated racism. Or, on a lighter note, how about those memes showing machine learning confusing blueberry muffins with Chihuahuas. Or mixing up images of bagels with pics of curled-up puppies?

Best practices can prevent some of these common pitfalls, but its essential to define them for the entirety of the data analysis process: before decisioning, during decisioning and after decisioning.

Lets take this step by step.

Before: It is all too common for companies to update an offering by adding a feature. But often they do so before completing meaningful data collection and analysis. Nobody has taken the time and resources to answer, Why are we adding this feature?

Before answering that all-important question, other questions need to be addressed. Are you seeing users doing this behavior naturally, already? What will the potential lift be? Is it worth the expense and time to tap into your engineering resources? What is the expected impact? What would this new feature ultimately mean to the future success of this product?

Youll need a lot of data to answer those queries. But lets say you culled it all and decided it was worthwhile to move ahead.

During: Youve launched that feature. There should be an ongoing stream of data that demonstrates whether or not the new feature is driving impact at the network level, at the publisher level, and at the user level.

Are you seeing the same impact across the board? Sometimes benefits to one can hurt another. Attention must be paid. Factor analysis is key. What are the factors at play that impact the analysis? Once identified, you need to determine if they are physically significant or not.

After: At this point, there are even more questions to address. What exactly is the impact? If you use A/B testing, can those short-term experiments provide dependable long-term forecasts? What lessons can you learn? Whether its a failure or success, how can it keep evolving? What are the new opportunities? What are the new behavioral changes youre seeing.

Machine learning for the long haul

There is a lot of data and oversight required to make a machine learning program truly viable. Its no wonder that many dont have the wherewithal to properly execute it and reap the benefits.

Here is the kicker: the data team doesnt make the decisions. The machine learning algorithm doesnt make the decisions. People make decisions. You can hire a fantastic squad of data scientists, and they can build and refine a machine learning model based on gobs of data that is 100% accurate. But for it to make any sort of difference to your business, you need to develop a strong workflow around it.

The best way to do that? Make sure data science teams are deeply integrated with different teams throughout your organization.

Establish a well-grounded data science practice, and you will see that machine learning can make the magic happen.

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Machine Learning Isnt Magic It Needs Strategy And A Human Touch - AdExchanger

ARLINGTON, Va. U.S. military researchers are asking industry to develop computers able not only to analyze large amounts of data automatically, but also communicate and cooperate with humans to resolve ambiguities and improve performance over time.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0052) on Thursday for the Environment-driven Conceptual Learning (ECOLE) project.

From industry, the DARPA ECOLE project seeks proposals in five areas: human language technology; computer vision; artificial intelligence (AI); reasoning; and human-computer interaction.

ECOLE will create AI agents able to learn from linguistic and visual input to enable humans and computers to work together to analyze image, video, and multimedia documents quickly in missions where reliability and robustness are essential.

Related: Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage

ECOLE will develop algorithms that can identify, represent, and ground the attributes that form the symbolic and contextual model for a particular object or activity through interactive machine learning with a human analyst. Knowledge of attributes and affordances, learned dynamically from data encountered within an analytic workflow, will enable joint reasoning with a human partner.

This acquired knowledge also will enable the machine to recognize never-before-seen objects and activities without misclassifying them as a member of a previously learned class, detect changes in known objects, and report these changes when they are significant.

System interaction with human intelligence analysts is expected to be symbiotic, with the systems augmenting human cognitive capabilities while simultaneously seeking instruction and correction to achieve accuracy.

Industry proposals should specify how symbolic knowledge representations will be acquired from unlabeled data, including the specifics of the learning mechanism; how these representations will be associated and reasoned within a growing body of knowledge; how the representations will be applied to human-interpretable object and activity recognition; and how the framework will permit collaboration with several analysts to resolve ambiguity, extend the set of known representations, and provide greater recognitional accuracy and coverage.

Related: Artificial intelligence (AI) to enable manned and unmanned vehicles adapt to unforeseen events like damage

The four-year ECOLE project with three phases; this solicitation concerns only the first and second phases. The first phase will create prototype agents that can pull relevant information out of unlabeled multimedia data, supplemented with human interaction.

These prototypes will demonstrate not only the ability to learn new concepts, but also to recombine previously learned attributes to recognize never-before-seen objects and activities. Systems also will be able to reason over similarities and differences in objects and activities.

The second phase of the ECOLE project will scale-up the framework to include several AI agents and human analysts to help deal with uncertain or contradictory information.

Computer interaction with human analysts will enable the system to learn to name and describe objects, actions, and properties to verify and augment their representations, and to acquire complex knowledge quickly and accurately from potentially sparse observations.

Related: Wanted: artificial intelligence (AI) and machine autonomy algorithms for military command and control

Humans and computers will work together primarily through the English language -- including words with several different meanings -- in a way that is readily understandable. The ECOLE project also will have two technical areas: distributed curriculum learning; and human-machine collaborative analysis.

Distributed curriculum learning involves multimedia data, and will use human partners provide feedback on the learning process. human-machine collaborative analysis will involve a human-machine interface (HMI) to improve ECOLE representations and analyze data such as multimedia and social media.

Companies interested should upload abstracts no later than 29 Sept. 2022, and full proposals by 14 Nov. 2022 to the DARPA BAA website at https://baa.darpa.mil.

Email questions or concerns to DARPA at ECOLE@darpa.mil. More information is online at https://sam.gov/opp/fd50cb65daf5493d886fa1ddc2c0dd77/view.

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Wanted: artificial intelligence (AI) and machine learning to help humans and computers work together - Military & Aerospace Electronics

Using AI, machine learning and advanced analytics to protect and optimize business | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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Using AI, machine learning and advanced analytics to protect and optimize business - Security Magazine

Artificial intelligence (AI) and machine learning (ML) are some of the most hyped enterprise technologies and have caught the imagination of boards, with the promise of efficiencies and lower costs, and the public, with developments such as self-driving cars and autonomous quadcopter air taxis.

Of course, the reality is rather more prosaic, with firms looking to AI to automate areas such as online product recommendations or spotting defects on production lines. Organisations are using AI in vertical industries, such as financial services, retail and energy, where applications include fraud prevention and analysing business performance for loans, demand prediction for seasonal products and crunching through vast amounts of data to optimise energy grids.

All this falls short of the idea of AI as an intelligent machine along the lines of 2001: A Space Odysseys HAL. But it is still a fast-growing market, driven by businesses trying to drive more value from their data, and automate business intelligence and analytics to improve decision-making.

Industry analyst firm Gartner, for example, predicts that the global market for AI software will reach US$62bn this year, with the fastest growth coming from knowledge management. According to the firm, 48% of the CIOs it surveyed have already deployed artificial intelligence and machine learning or plan to do so within the next 12 months.

Much of this growth is being driven by developments in cloud computing, as firms can take advantage of the low initial costs and scalability of cloud infrastructure. Gartner, for example, cites cloud computing as one of five factors driving AI and ML growth, as it allows firms to experiment and operationalise AI faster with lower complexity.

In addition, the large public cloud providers are developing their own AI modules, including image recognition, document processing and edge applications to support industrial and distribution processes.

Some of the fastest-growing applications for AI and ML are around e-commerce and advertising, as firms look to analyse spending patterns and make recommendations, and use automation to target advertising. This takes advantage of the growing volume of business data that already resides in the cloud, cutting out the costs and complexity associated with moving data.

The cloud also lets organisations make use of advanced analytics and compute facilities, which are often not cost-effective to build in-house. This includes the use of dedicated, graphics processing units (GPUs) and extremely large storage volumes made possible by cloud storage.

Such capabilities are beyond the reach of many organisations on-prem offerings, such as GPU processing. This demonstrates the importance of cloud capability in organisations digital strategies, says Lee Howells, head of AI at advisory firm PA Consulting.

Firms are also building up expertise in their use of AI through cloud-based services. One growth area is AIOps, where organisations use artificial intelligence to optimise their IT operations, especially in the cloud.

Another is MLOps, which Gartner says is the operationalisation of multiple AI models, creating composite AI environments. This allows firms to build up more comprehensive and functional models from smaller building blocks. These blocks can be hosted on on-premise systems, in-house, or in hybrid environments.

Just as cloud service providers offer the building blocks of IT compute, storage and networking so they are building up a range of artificial intelligence and machine learning models. They are also offering AI- and ML-based services which firms, or third-party technology companies, can build into their applications.

These AI offerings do not need to be end-to-end processes, and often they are not. Instead, they provide functionality that would be costly or complex for a firm to provide itself. But they are also functions that can be performed without compromising the firms security or regulatory requirements, or that involve large-scale migration of data.

Examples of these AI modules include image processing and image recognition, document processing and analysis, and translation.

We operate within an ecosystem. We buy bricks from people and then we build houses and other things out of those bricks. Then we deliver those houses to individual customers, says Mika Vainio-Mattila, CEO at Digital Workforce, a robotic process automation (RPA) company. The firm uses cloud technologies to scale up its delivery of automation services to its customers, including its robot as a service, which can run either on Microsoft Azure or a private cloud.

Vainio-Mattila says AI is already an important part of business automation. The one that is probably the most prevalent is intelligent document processing, which is basically making sense of unstructured documents, he says.

The objective is to make those documents meaningful to robots, or automated digital agents, that then do things with the data in those documents. That is the space where we have seen most use of AI tools and technologies, and where we have applied AI ourselves most.

He sees a growing push from the large public cloud companies to provide AI tools and models. Initially, that is to third-party software suppliers or service providers such as his company, but he expects the cloud solution providers (CSPs) to offer more AI technology directly to user businesses too.

Its an interesting space because the big cloud providers spearheaded by Google obviously, but very closely followed by Microsoft and Amazon, and others, IBM as well have implemented services around ML- and AI-based services for deciphering unstructured information. That includes recognising or classifying photographs or, or translation.

These are general-purpose technologies designed so that others can reuse them. The business applications are frequently very use-case specific and need experts to tailor them to a companys business needs. And the focus is more on back-office operations than applications such as driverless cars.

Cloud providers also offer domain-specific modules, according to PA Consultings Howells. These have already evolved in financial services, manufacturing and healthcare, he says.

In fact, the range of AI services offered in the cloud is wide, and growing. The big [cloud] players now have models that everyone can take and run, says Tim Bowes, associate director for data engineering at consultancy Dufrain. Two to three years ago, it was all third-party technology, but they are now building proprietary tools.

Azure, for example, offers Azure AI, with vision, speech, language and decision-making AI models that users can access via AI calls. Microsoft breaks its offerings down into Applied AI Services, Cognitive Services, machine learning and AI infrastructure.

Google offers AI infrastructure, Vertex AI, an ML platform, data science services, media translation and speech to text, to name a few. Its Cloud Inference API lets firms work with large datasets stored in Googles cloud. The firm, unsurprisingly, provides cloud GPUs.

Amazon Web Services (AWS) also provides a wide range of AI-based services, including image recognition and video analysis, translation, conversational AI for chatbots, natural language processing, and a suite of services aimed at developers. AWS also promotes its health and industrial modules.

The large enterprise software and software-as-a-service (SaaS) providers also have their own AI offerings. These include Salesforce (ML and predictive analytics), Oracle (ML tools including pre-trained models, computer vision and NLP) and IBM (Watson Studio and Watson Services). IBM has even developed a specific set of AI-based tools to help organisations understand their environmental risks.

Specialist firms include H2O.ai, UIPath, Blue Prism and Snaplogic, although the latter three could be better described as intelligent automation or RPA companies than pure-play AI providers.

It is, however, a fine line. According to Jeremiah Stone, chief technology officer (CTO) at Snaplogic, enterprises are often turning to AI on an experimental basis, even where more mature technology can be more appropriate.

Probably 60% or 70% of the efforts Ive seen are, at least initially, starting out exploring AI and ML as a way to solve problems that may be better solved with more well-understood approaches, he says. But that is forgivable because, as people, we continually have extreme optimism for what software and technology can do for us if we didnt, we wouldnt move forward.

Experimentation with AI will, he says, bring longer-term benefits.

There are other limitations to AI in the cloud. First and foremost, cloud-based services are best suited to generic data or generic processes. This allows organisations to overcome the security, privacy and regulatory hurdles involved in sharing data with third parties.

AI tools counter this by not moving data they stay in the local business application or database. And security in the cloud is improving, to the point where more businesses are willing to make use of it.

Some organisations prefer to keep their most sensitive data on-prem. However, with cloud providers offering industry-leading security capabilities, the reason for doing this is rapidly reducing, says PA Consultings Howells.

Nonetheless, some firms prefer to build their own AI models and do their own training, despite the cost. If AI is the product and driverless cars are a prime example the business will want to own the intellectual property in the models.

But even then, organisations stand to benefit from areas where they can use generic data and models. The weather is one example, image recognition is potentially another.

Even firms with very specific demands for their AI systems might benefit from the expansive data resources in the cloud for model training. Potentially, they might also want to use cloud providers synthetic data, which allows model training without the security and privacy concerns of data sharing.

And few in the industry would bet against those services coming, first and foremost, from the cloud service providers.

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Putting artificial intelligence and machine learning workloads in the cloud - ComputerWeekly.com