Category Archives: Artificial Intelligence

What are the first impressions that befall to your brain when you ponder about the judicial system in your country? If its prolix, expensive, biased and occasional injustice, then youre not only to think like that. Wouldnt it be incredible if your first associations were instead competence, truth and justice? Since the legal process can be abstractly observed as inserting data about evidence and laws and obtaining a judgment, some experts consider of fully automating it with AI. AI systems that indefatigable practice the same high legal standards to every decision without prejudice, exhaustion or lack of the advance acquaintance.

Transparent, handy and adequate rule of law heightens the economy, pulls foreign investments, updates the work-flows of the whole judicial system, and as if that wasnt enough it prevails one of the foundations of democracies globally. In Europe, for instance, the Estonian court system is rated one of the most effective with its robot judges, programmed court procedures.

Under the current COVID-19 dilemma, the need of e-court system is vital. It has been extensively discussed whether AI is going to supersede the judges and legal prosecutors in the future. Based on the Estonian practice thus far, however, AI is here to support the judicial system so that those that serve in the law field can focus on more constraining elements that genuinely need human interplay.

Now, the revolution of AI presents an excellent opportunity to remodel the judicial system into a mesmerizingly fast, effective, and an unbiased array of legal services available to all citizens.

In Estonia, the addition of the e-Filling court papers was started in 2005. Since then, e-judicial system and our expectations towards it have grown quite remarkably. As soon as people have securely proved themselves and accessed the e-judicial platform, they can submit any kind of cases with pieces of evidence and other relevant data online. The provided material will be shared between establishments that are linked to the case and courts can start proceeding with related records. These communications are based on the once-only policy which means that copies of data are not permitted in court databases.

The e-judicial system enables courts to send citizens different documents, while instant notifications ensure judges that all files have been successfully carried. Every document is stamped and holds a secure e-signature. Moreover, classified data can be encrypted to ensure courts that no third party is capable to access the record. This supported the Estonian e-judicial system to earn the honor of a stable and efficient array of services.

Currently, the number of judges in Estonia remains the same as twenty years ago. While, the number of cases registered in Estonian courts has increased as doubled over that time span. Given the complications of the judicial system from the local to the European Union level, the burden on the court system appears unlikely to diminish admittedly, the opposite appears much more probable. It indicates that this is the ideal time for AI companies to develop systems that help judicial experts to give less time on time-consuming tasks and find judgments to supersede with automated systems. Applications of artificial intelligence can predict the outcomes of processes and identify new patterns. AI is competent in making independent judgments within more common court procedures that would engage judges for days.

After reading above, one might wonder that promoting the court systems is good to go, however surely these resolutions expensive. In fact, the Estonian e-court system is operating on one of the most economical per capita budgets across the whole European Union. The AI judicial system indicates how entire states can advantage from automatic approaches.

Prompt incorporation of Artificial Intelligence techniques is opening up a wide range of opportunities for judges and lawyers.If the cooperation between diverse sectors is stable, AI could reduce the amount of data and evidence input, present a more substantial and extensive overview of all relevant pieces of evidence across state registries, and surely, along with saving time and money Al could overcome the red tape between courts and residents.

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Applications of Artificial Intelligence in the Judicial System - Legal Examiner

Is artificial intelligence a key ingredient to inspire rural children to become entrepreneurs?

Microsoft Chief Technology Officer Kevin Scott rise to his current post is about as unlikely as you will find. He grew up in Gladys, Virginia, a town of a few hundred people. He loved his family and his hometown to such an extent that he did not aspire to leave. He caught the technology bug in the 1970s by chance, and that passion would provide a ticket to bigger places that he did not initially seek.

The issue was one of opportunity. In his formative years, jobs were decreasing in places like Gladys just as they were increasing dramatically in tech hubs like Silicon Valley. After pursuing a PhD in computer science at the University of Virginia, he left in 2003 prior to completing his dissertation to join Google. He would rise to become a Senior Engineering Director there. He left Google for LinkedIn in 2011. He would eventually rise to become the Senior Vice President of Engineering & Operations at LinkedIn. From LinkedIn he joined Microsoft three and a half years ago as CTO. He is deeply satisfied with the course of his career and its trajectory, but part of him laments that it took him so far from his roots and the hometown that he loves.

As he reflected further on this conundrum, he put his thoughts to paper and published the book, Reprogramming the American Dream in April, co-authored by Greg Shaw. As he noted in a conversation I recently had with him, Silicon Valley is a perfectly wonderful place, but we should be able to create opportunity and prosperity everywhere, not just in these coastal urban innovation centers.

Scott believes that machine learning and artificial intelligence will be key ingredients to aiding an entrepreneurial rise in smaller towns across the United States. These advances will place less of a burden on companies to hire employees in the small towns, as some technical development will be conducted by the bots. He also hopes that as some of these businesses blossom, more kids will be inspired to start their own businesses powered by technology, creating a virtuous cycle of sorts.

The biggest impediment to this dream boils down to more basic elements, however. There is just no way that you can reasonably educate your kids and attract and retain really great employees to these jobs and to even run the businesses themselves unless you have good broadband connectivity in all of these places, notes Scott. 25 million people in the United States do not have adequate access to broadband. 19 million of those are in these rural communities. So that is something we definitely have to fix. Scott also says that there must be redoubled efforts for venture capitalists to invest in businesses in non-traditional towns and cities. He highlights the work that Steve Case has done with his Rise of the Rest Seed Fund through Revolution Capital.

Scott underscores that venture capital is not enough. It will require a private public partnership. I think we could choose to say that we want to pick one of these big, hairy, audacious goals that AI technologies and machine learning could help reach and pour a little bit of our national wealth into this in a coordinated way, says Scott. [We can] create a great collaboration between private companies, the academy and the government to solve a big problem for the public good like, potentially, ubiquitous high quality, low-cost health care. We could do something that is even better than the Apollo program.

Some might think that artificial intelligence is too esoteric and complicated to teach to children so that they are fluent enough to leverage the technology of the future. Scott argues otherwise. He says, If we can harness this ability that we have to teach each other, we can certainly teach machines how to solve problems, which makes programming or harnessing a computer's power even more accessible than it has ever been and certainly a thing and a set of skills that are absolutely approachable for even very young kids.

Scott and his wife have created the Scott Foundation, which helps create opportunities for children to achieve self-sufficiency and lifelong success. Not so surprisingly, Scott believe technology is a major ingredient of that future success, as well. His day job and his foundation work are sources of optimism. At a time when many lament that the rise of artificial intelligence will eliminate many jobs, Scott believes those losses will be more than offset by those new businesses created in all corners of the United States leveraging AI and other technical advances.

Peter Highis President ofMetis Strategy, abusiness and IT advisory firm. His has written two bestselling books, moderates theTechnovationpodcast series, and speaks at conferences around the world. Follow himon Twitter@PeterAHigh.

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Microsoft CTO Kevin Scott Believes Artificial Intelligence Will Help Reprogram The American Dream - Forbes

Perfectly imperfect

What is the acceptable failure rate of an airplane? Well, it is not zero no matter what how hard we want to believe otherwise. There is a number, and it is a very low number. When it comes to machines, computers, artificial intelligence, etc., they are perfectly imperfect. Mistakes will be made. Poor recommendations will occur. AI will never be perfect. That does not mean they do not provide value. People need to understand why machines may mistakes and set their beliefs accordingly. This means understanding the three key areas on why AI fails: implicit bias, poor data, and expectations.

The first challenge is implicit bias, which are the unconscious perceptions people have that cloud thoughts and actions. Consider, the recent protests on racial justice and police brutality and the powerful message that Black Lives Matter. The Forbes article AI Taking A Knee: Action To Improve Equal Treatment Under The Law is a great example of how implicit bias has played a role in the discrimination and just how hard (but not impossible) it is to use AI to reduce prejudice in our law enforcement and judicial systems. AI learns from people. If implicit bias is in the training, then the AI will learn this bias. Moreover, when the AI performs work, that work will reflect this bias even if the work is for social good.

Take for example the Allegheny Family Screening Tool. It is meant to predict which welfare children might be at risk from foster parent abuse. The initial rollout of this solution had some challenges though. The local Department of Human Services acknowledged that the tool might have racial and income bias. Triggers like neglect were often confused or misconstrued by associating foster parents who lived in poverty with inattention or mistreatment. Since learning of these problems, tremendous steps were taken to reduce the implicit bias in the screening tool. Elimination is much harder. When it comes to bias, how do people manage the unknown unknowns? How is social context addressed? What does right or fair behavior mean? If people cannot identify, define, and resolve these questions, then how will they teach the machine? This is a major driver AI will be perfectly imperfect because of implicit bias.

Coronavirus 2019 - ncov flu infection - 3D illustration

The second challenge is data. Data is the fuel for AI. The machine trains through ground truth (i.e. rules on how to make decisions, not the decisions themselves) and from lots of big data to learn the patterns and relationships within the data. If our data is incomplete or flawed, then AI cannot learn well. Consider COVID-19. John Hopkins, The COVID Tracking Project, U.S. Centers for Disease Control (CDC), and the World Health Organization all report different numbers. With such variation, it is very difficult for an AI to gleam meaningful patterns from the data let alone find those hidden insights. More challenging, what about incomplete or erroneous data? Imagine teaching an AI about healthcare but only providing data on womens health. That impedes how we can use AI in healthcare.

Then there is a challenge in that people may provide too much data. It could be irrelevant, unmeaningful, or even a distraction. Consider when IBM had Watson read the Urban Dictionary, and then it could not distinguish when to use normal language or to use slang and curse words. The problem got so bad that IBM had to erase the Urban Dictionary from Watsons memory. Similarly, an AI system needs to hear about 100 million words to become fluent in a language. However, a human child only seems to need around 15 million words to become fluent. This implies that we may not know what data is meaningful. Thus, AI trainers may actually focus on superfluous information that could lead the AI to waste time, or even worse, identify false patterns.

The third challenge is expectations. Even though humans make mistakes, people still expect machines to be perfect. In healthcare, experts have estimated that the misdiagnosis rate may be as high as 20%, which means potentially one out of five patients are misdiagnosed. Given this data as well as a scenario where an AI assisted diagnosis may have an error rate of one out of one hundred thousand, most people still prefer to see only the human doctor. Why? One of the most common reasons given is that the misdiagnosis rate of the AI is too high (even though it is much lower than a human doctor.) People expect AI to be perfect. Potentially even worse, people expect the human AI trainers to be perfect too.

On March 23, 2016, Microsoft launched Tay (Thinking About You), a Twitter bot. Microsoft had trained its AI to the level of language and interaction of a 19-year-old, American girl. In a grand social experiment, Tay was released to the world. 96,000 tweets later, Microsoft had to shut Tay down about 16 hours after launch because it had turned sexist, racist, and promoted Nazism. Regrettably, some individuals decided to teach Tay about seditious language to corrupt it. In conjunction, Microsoft did not think to teach Tay about inappropriate behavior so it had no basis (or reason) to know that something like inappropriate behavior and malicious intent might exist. The grand social experiment resulted in failure, and sadly, was probably a testament more about human society than the limitations of AI.

nobodys perfect

Implicit bias, poor data, and people expectations show that AI will never be perfect. It is not the magic bullet solution many people hope to have. AI can still do some extraordinary things for humans like restore mobility to a lost limb or improve food production while using less resources. People should not discount the value we can get. We should always remember: AI is perfectly imperfect, just like us.

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Perfectly Imperfect: Coping With The Flaws Of Artificial Intelligence (AI) - Forbes

Can your data talk intelligently? AI plugged into data management systems aims to do just that!

Intelligent analytics offers a classic approach to discover the hidden intelligence behind historical and real-time data. This myriad suite of analytical techniques and algorithms can parse mind-boggling amounts of data generated in real-time to discover the hidden gems that are often missed or go undetected by traditional statistical methods.

The methodology of mixing intelligence with analytics reaches far beyond. It erects the foundation in algorithmic methods removing any bias introduced by an individual analyst. Whats more, the sheer volume of data adds to the veracity and accuracy of the results, rather than causing an unnecessary air of confusion for the analyst.

An artificial intelligence (AI) and analytics platform encapsulate the means to derive untapped value from the wealth of information, data constantly generates. While advanced analytics helps enterprises to uncover insights on current business processes and even draw predictions from historical information silos, AI acts as a force multiplier on this data crunching by pledging machine learning capabilities into these data models.

The best artificial intelligence algorithms and analytics software leverage machine learning solutions into big data platform. This way they transform data into intelligent pieces of information, self-service data visualization dashboards, automation-ready capabilities to maximize revenue and operational efficiencies.

AI can actually transform data into an intelligent piece of Intelligence

1. Unearthing new insights from data analytics

Artificial Intelligence excels in finding hidden patterns and insights from large datasets which are often unseen from human eyes, this is done at an unprecedented speed and scale. AI-powered tools exist answering the questions about your enterprise operations, for instance, which operations cycle had the quickest turn-around in a specific quarter.

2. Deploy analytics to predict data outcomes

AI-powered algorithms analyze data from multiple sources offering predictions on an enterprises next strategic move. It can also deep dive into data to share insights about your customers letting you know about their preferences, and which marketing channels would be the best to target them.

3. Unifying data across Platforms

Artificial Intelligence unifies data captured from different sources and platforms, accelerating data-driven innovation across data science, business analytics and data engineering categories.

Data analytics software

Think business intelligence gathered from a data analytics software that identifies patterns and formulates data relationships. This paves way for actionable alerts, smart data discovery and interactive dashboards, using a comprehensive set of data analytics software on an enterprise-grade analytics platform.

Machine learning and predictive analytics platform

An able platform lets you analyze structured and unstructured big data stored in data management platforms and external sources. AI and open-source data analytics platforms combine open-source machine learning with self-service analytics and predictive analytics to achieve data intelligence.

Natural language processing and text mining

Unstructured data explains stories, sentiments, emotions of your customers, employees and stakeholders. NLP and Text mining extracts terms and concepts from brochures, legal documents, emailers, social media messages, videos, audio files, web pages to unlock the value hidden in unstructured text and yield valuable business insights.

Interactive visualizations

Data visualization is the graphic representation of data. Interactive data visualizations and rich interactive dashboards are the major takeaways from Intelligent Analytics helping enterprises know their data more personally.

AI solution for sentiment analysis

Intelligent data analytics helps an enterprise to understand and highlight what is the peoples perception on social networks and the web about its products and services. Intelligent analytics is thus a blessing to enterprises for targeted customer servicing, customer engagement and retention.

In crux, AI blended data analytics aims to make the enterprise more efficient and productive thereby increasing its brand loyalty, drive revenues and eliminate the need for manual data processing mechanisms. With customised business insights that are accessible and relatable to the most critical objectives of the enterprise, Intelligent Analytics is here to stay.

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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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Propelling Data Analytics with the Power of Artificial Intelligence - Analytics Insight

This report studies the levels, evolution, geography, knowledge base and quality of AI research in the COVID-19 mission field using a novel dataset taken from open preprints sites arXiv, bioRxiv and medRxiv, which we have enriched with geographical, topical and citation data.

Although there has been rapid growth in the levels of AI research to tackle COVID-19 since the beginning of the year, AI remains underrepresented in this area compared to its presence in research outside of COVID-19. So far in 2020, 7.1 per cent of research on COVID-19 references AI, while 12 per cent of research on topics outside COVID-19 references it. After growing rapidly earlier in the year, the share of AI papers in COVID-19 research has stagnated in recent weeks.

More than a third of publications to tackle COVID-19 involve predictive analyses of patient data and in particular medical scans. AI is also being deployed to analyse social media data, predict the spread of the disease and develop biomedical applications.

China, the US, the UK, India and Canada are the global leaders in the development of AI applications to tackle COVID-19 research, accounting for 62 per cent of the institutional participations for which we have geographical data. China in particular is overrepresented in COVID-19 AI research. We have also identified a substantial number of publications involving institutions that we are unable to match with the global research institution database we are using. This is consistent with the idea that new actors are entering the COVID-19 mission field.

AI and non-AI researchers working in COVID-19 tend to draw on different bodies of knowledge. AIs share of citations to computer science is five times higher than outside and its share of citations to medicine is a third lower. These differences hold, even after we control for the topic within COVID-19 that different publications focus on .

In general, AI papers to tackle COVID-19 tend to receive less citations than other papers in the same topic. The population of AI researchers active in the COVID-19 mission field also tends to have a less established track record proxied through the citations they have received in recent years. This result holds when we compare researchers working in the same topics, suggesting that it is not simply driven by variation in the citation behaviours of different communities and disciplines.

Our analysis highlights the velocity with which research communities including AI researchers are mobilising to tackle the COVID-19 pandemic. We find many opportunities to apply powerful AI algorithms to prevent, diagnose and treat the virus. At the same time, deep learning algorithms reliance on big datasets, difficulties interpreting their findings, and a disconnect between AI researchers and relevant bodies of knowledge in the medical and biological sciences may limit the impact of AI in the fight with COVID-19. The persistent underrepresentation of AI research in the COVID-19 field we evidence, and its focus on computer vision analyses that play to the strengths of current algorithms, but require substantial investments in hardware and changing how hospitals work, are consistent with the notion that AIs may play a limited role tackling this pandemic.

There is also the risk that researchers facing low barriers to entry into the field may produce low-quality contributions making it harder to find valuable studies and discourage interdisciplinary contributions that could take longer to develop. Our finding that AI research tends to be less cited than other research, even inside the same publication topics, and that AI researchers entering the field have a weaker track record on average than others, lends some support to these concerns.

In the shorter term, creating bigger higher-quality open datasets related to COVID-19 could make it easier to deploy state-of-the-art deep learning algorithms. Spurring interdisciplinary collaborations, bringing together AI researchers and subject experts, may help to prioritise those AI applications with the greatest relevance and value. It might also reduce the risk of AI imperialism; where AI researchers ignore relevant bodies of knowledge about the complex biological and social systems where their techniques will be applied, reducing their value and creating unintended consequences. We also need technological and social solutions for the challenge of navigating a vast and fast-growing body of research of uncertain quality. Going forward, research funders should encourage the development of AI algorithms that are easier to deploy in small-data, high-stakes domains.

Novel data sources and methods, such as those we have used in our analysis, can play an important role in informing these strategies.

The data set used in this report is open for other researchers to analyse and build on.

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Artificial Intelligence and the Fight Against COVID-19 - nesta

The Artificial Intelligence In Fashion Market report upholds the future market predictions related to Artificial Intelligence In Fashion market size, revenue, production, Consumption, gross margin and other substantial factors. It also examines the role of the prominent Artificial Intelligence In Fashion market players involved in the industry including their corporate overview. While emphasizing the key driving factors for Artificial Intelligence In Fashion market, the report also offers a full study of the future trends and developments of the market.

The global artificial intelligence in fashion market accounted for US$ 270.0 Mn in 2018 and is expected to grow at a CAGR of 36.9% over the forecast period 2019-2027, to account for US$ 4,391.7 Mn in 2027. Driving factors such as availability of massive amount of data due to increasing proliferation of digital services across the globe, and real time consumer behavior insights and increased operational efficiency are driving the adoption of AI in fashion industry will drive the market during the forecast period and have a high impact in the short term. However, factors such as concerns related to data privacy and security is anticipated to hinder the market growth in the coming years.

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The overall artificial intelligence in fashion market size has been derived using both primary and secondary source. The research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the artificial intelligence in fashion market. It also provides the overview and forecast for the global artificial intelligence in fashion market based on all the segmentation provided with respect to five major reasons such as North America, Europe, Asia-Pacific, the Middle East and Africa, and South America. Also, primary interviews were conducted with industry participants and commentators in order to validate data and analysis. The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers, and national sales managers, and external consultant such as valuation experts, research analysts and key opinion leaders specializing in the artificial intelligence in fashion market. Some of the players present in artificial intelligence in fashion market are Adobe Inc., Amazon Web Services, Inc., Catchoom Technologies S.L., Facebook, Inc., Google LLC, Huawei Technologies Co., Ltd., IBM Corporation, Microsoft Corporation, Oracle Corporation, and SAP SE among others.

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AI integration in fashion plays a crucial role in sales, marketing, and customer-focused purposes. Initial adopters point toward the key impacts of technology in improving customer experience and decent growth in company revenue. Elevated customer experience helps the retailer to crack entirely new tactics of customer engagement and communication. With AI integration, the retailers can precisely spot the customers expected needs at precise times and offer the appropriate product to gain a competitive advantage. Some of the past initiatives taken in the fashion industry sector which has revolutionize the use of AI in the sector are North Face leveraging IBM Watsons ML technology to recommend more personalized apparel to the customers. Further, eBays AI integration helps their sellers sell more by better inventory management and pricing recommendations. Also, several fashion brands are leveraging chatbot to improve the customer experience; Tommy Hilfigers Facebook Messenger chatbot offers consumers a custom-made and interactive shopping experience than customary online shopping. Besides, Sephora, Amazon, Target, ASOS, Stitch Fix, and Olay are other renowned fashion industry names in the list who have already integrated the AI solution to boost their sales and marketing strategy.

Global Artificial Intelligence In Fashion Market: Drivers and Restraints: This section of the Artificial Intelligence In Fashion Market Analysis report we are covering various drivers and restraints that have affected the global Artificial Intelligence In Fashion market. The complete study of plentiful drivers of the market enables market professionals to get a clear viewpoint of the Artificial Intelligence In Fashion market share, which consists of Artificial Intelligence In Fashion industry environment, advancement market, product innovations, latest developments, and Artificial Intelligence In Fashion market risks.

The artificial intelligence in fashion market has been segmented on the basis of offerings, deployment, application, end-user industry, and geography. The artificial intelligence in fashion market based on offerings is sub-segmented into solution and services. The solution segment is expected to hold the prime market share in the artificial intelligence in fashion market. The artificial intelligence in fashion market on the basis of deployment is segmented into cloud and on-premise. The cloud segment led the artificial intelligence in fashion market and it is anticipated to continue its dominance during the forecast period. The market for artificial intelligence in fashion by application is further segmented into product recommendation, virtual assistant, product search and discovery, creative designing and trend forecasting, customer relationship management, and others. The product recommendation segment led the artificial intelligence in the fashion market in 2018 and is expected to continue its dominance during the forecast period.

A Pin-point overview of TOC of Artificial Intelligence In Fashion Market are:

Overview and Scope of Artificial Intelligence In Fashion Market

Artificial Intelligence In Fashion Market Insights

Industry analysis - Porter's Five Force

Company Profiles

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Artificial Intelligence In Fashion Market Detailed Analysis of Current Industry Figures with Forecasts Growth By 2027 - CueReport

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Request for Technavio's latest reports on directly and indirectly impacted markets. Market estimates include pre- and post-COVID-19 impact on the Artificial Intelligence-as-a-Service (AIaaS) Market Download free sample report

The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

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Growing adoption of cloud based solutions has been instrumental in driving the growth of the market.

Technavio's custom research reports offer detailed insights on the impact of COVID-19 at an industry level, a regional level, and subsequent supply chain operations. This customized report will also help clients keep up with new product launches in direct & indirect COVID-19 related markets, upcoming vaccines and pipeline analysis, and significant developments in vendor operations and government regulations. https://www.technavio.com/report/report/artificial-intelligence-as-a-service-market-industry-analysis

Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation

Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. The artificial intelligence-as-a-service (AIaaS) market report covers the following areas:

This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.

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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights

Table of Contents:

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by End-user

Customer Landscape

Geographic Landscape

Drivers, Challenges, and Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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COVID-19 Impact and Recovery Analysis- Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024 | Growing Adoption of Cloud Based Solutions to...

Futuris looks at how the relationship between man and machine in modern manufacturing is evolving through the adoption of Artificial Intelligence and automation.

No one knows the job better than the person who is doing it - that is the idea behind a package of novel ideas designed to make the most of factory workers' knowledge and experience.

In Seinajoki, Finland, metal company Prima Power is trialing two of the EUs Factory2Fit project solutions.

This 4m study explores new ways for people and machines to work together.

Dr Eija Kaasinen from technical research centre VTT says the aim is to put people at the centre and to enable them to participate in designing their own work environment.

Globally, automation and robotics are transforming manufacturing as part of the fourth industrial revolution. But this doesn't mean the human element is removed from work.

"Of course there are manual elements - but the work is changing towards knowledge work," explains Kaasinen. "It's more like working with the virtual counterparts of the physical things in the physical world."

A Pre-training Solution, for example, uses 3D models and cloud-based tutorials, while a so-called Knowledge Sharing solution makes the most of all the experience a worker gathers while running complex machinery, especially when something goes wrong, as Prima Power's Mariia Kreposna explains.

"So here the operator can open the additional dialogue box to get extra information about the situation. This is done by sharing the additional text, description, pictures or videos so the idea is in the future whenever the alarm with the same code happens, the operator will be able to learn not only the standard remedies but also other possible reasons and how to prevent this alarm happening in the future."

At the Elekmerk factory in Keuruu, Finland, workers have tested the Worker Feedback Dashboard - a biometric monitoring tool - like a fitbit - and an app.

It charts someone's work achievements and their well being - such as sleep and steps taken per day - and shows how the two can be linked.

"When we interviewed factory workers during the project we heard that often they had negative feedback when something is not going well," says VTT's Pivi Heikkil.. "So we wanted to develop an application that would also give you positive feedback of the fluency of your work and your accomplishments, so feedback of the things that are going well."

Ville Vuarola was one of five workers who wore the wristband for the three-month pilot scheme. He was happy to take part and says he was surprised at how a good night's sleep had a positive impact on his job.

"I was surprised to see how sleeping well influenced my work performance. Together with leisure activities, sleep was really important for my general performance at work," he says.

Of all the Factory2Fit solutions, this was the one that proved the most controversial with fears expressed over the possible misuse of workers' data.

But Pivi Heikkil says these concerns are unfounded.

"When we are developing these kind of solutions we always consider the ethics," she says, "and I want to stress and highlight that this should always be voluntary."

The data gathered is kept on a separate server, not in the factory system.

Researchers expect at least some of their Factory2Fit solutions to be commercially available by the end of next year.

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Artificial Intelligence: how man and machine are progressively working as one - Euronews

REJOINT, an Italian medical implant manufacturer, is introducing mass customization and therapy personalization through a combination of additive manufacturing technology with artificial intelligence.

Specifically, the company will be using GE Additive Arcams Electron Beam Melting (EBM) technology and computerized analysis of intraoperative and post-operative data collection through IoT-connected sensorized wearables. This will help REJOINT in fabricating personalized medical devices for patients in the form of knee implants.

Combining AI with 3D printing for medical devices

Based in Bologna, Italy, REJOINT was founded in 2015 by a team with significant experience in the orthopedic sector, with the company recently entered the knee arthroplasty market.

REJOINTs objective centers on providing patients with a customized medical solution; both additive manufacturing and artificial intelligence are integral to its goals and growth strategy. For producing its personalized prostheses, the company opted to use 3D printing technology from GE Additive.

When it came to additive manufacturing, we were initially undecided about the most suitable solution for personalized cobalt-chrome prosthetics and were evaluating DMLM and EBM. Both modalities, in fact offer a good level of resolution and quality, but we ultimately opted for the GE Additive Arcam EBM Q10plus system. The knowledge and industrialization support that GE was able to provide us and the professional experience of their local team here in Italy also informed our decision, comments Gian Guido Riva, CEO at REJOINT.

The market for knee implants is estimated at around five million implants per year worldwide. In 2011 the number of surgical procedures was 150 per 100,000 inhabitants, with peaks of 250 in some markets such as Austria and Switzerland.

Until recently, the knee arthroplasty market solely consisted of standard prosthetic systems, with only a limited range of sizes available. Correct and precise sizing and positioning is a critical factor for the type of prostheses. Typically, knee joints have to withstand point loads that can reach levels of over 300 kilograms. Minimal dimensional differences between the patients bone elements and an implant can lead to pain and inflammation.

For the patient, inadequate sizing means constant awareness of the presence of an artificial joint, as well as leading to muscle and ligament decay. Patient feedback can sometimes reflect issues with an implant. Dissatisfaction is often largely related to the suboptimal sizing of the implanted prosthesis.

As such, to produce tailored prostheses for the patient, REJOINT leverages the design freedom offered by additive manufacturing. The company begins its process by 3D modelling the patients CT scan, and then uses algorithms to analyze the images and identify the most suitable size for each specific case. Artificial intelligence compares the unique anatomy of a patient on several thousand prosthetic dimensions, each with as many dimensional variables in specific areas of the implant.

The optimal configuration is identified and offered to the surgeon, for positioning both the prosthetic components and for simulating the operation. The analytical process forms the core of the prosthesis production process and for patient-specific tools for the planning of the intervention which is carried out with the support of computer-aided surgery tools.

Having all this data made us realize that we could link it to the information recorded during the operation. And in turn, this data could still be further improved upon if we could collect through the use of wearable devices (such as sensorized headbands and socks), both pre- and post-operative measurements, on how the patient loads their limb or bends their knee, until post-operative evaluation questionnaires have been completed, continues Riva.

Professor Maurilio Marcacci, who is head of the Joint Knee Reconstruction Centre at Humanitas Research Hospital in Milan and performed the first implant, claims that the initial application of this technology has achieved a high degree of patient satisfaction.

Currently, REJOINT is in the process of obtaining FDA clearance, which is expected in the first half of 2021. Certification will mean access to the US market, which accounts for 62 percent of the world market for orthopedic devices and more than 70 percent of the value of the global market for knee implants. Furthermore, REJOINT is working with GE Additive to reduce powder-based production costs, focusing on the reduction of cycle times and the optimization of parameters including through the development of remote production control stations.

Personalized healthcare with 3D printing

A key advantage of 3D printing in the medical sector is its ability for providing patient-specific healthcare. For example, customized prosthesis is being provided in Sierra Leone by dutch nonprofit organization 3D Sierra Leone. Using 3D scanning to measure each patient, a tailored device is then produced for the amputees in the country, who are without access to relevant medical care.

Outside of prostheses, 3D printing has also been used by a doctor at Cleveland Clinic to produce patient-specific airway stents, which have also received clearance from the FDA for implantation in patients.

3D printing can also play a significant role in patient-specific preoperative planning. Earlier in June, we reported on an anaesthesia team in Israel that recently used 3D printing and virtual reality to produce an exact model of the airway of a 7-year-old girl, as part of an operation to remove a section of her lung.

The nominations for the 2020 3D Printing Industry Awards are now open. Who do you think should make the shortlists for this years show? Have your say now.

Subscribe to the 3D Printing Industry newsletter for the latest news in additive manufacturing. You can also stay connected by following us on Twitter and liking us on Facebook.

Looking for a career in additive manufacturing? Visit 3D Printing Jobs for a selection of roles in the industry.

Featured image showsArcam Q10plus. Photo via GE Additive.

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REJOINT is developing knee replacements with additive manufacturing and artificial intelligence - 3D Printing Industry

This report additionally covers the effect of COVID-19 on the worldwide market. The pandemic brought about by Coronavirus (COVID-19) has influenced each part of life all inclusive, including the business segment. This has brought along a several changes in economic situations.

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