Daily Archives: June 19, 2020

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.

A report on Artificial Intelligence (AI) market compiled by Brand Essence Market Research provides a succinct analysis regarding the values and trends existing in the current business scenario. The study also offers a brief summary of market valuation, market size, regional outlook and profit estimations of the industry. Furthermore, the report examines the competitive sphere and growth strategies of leading players in the Artificial Intelligence (AI) market. Download Premium Sample of the Report: https://industrystatsreport.com/Request/Sample?ResearchPostId=597&RequestType=Sample

TheMajorPlayersCovered in this Report:Intel, Nvidia, Samsung Electronics, Xilinx, Micron Technology, IBM, Microsoft, Google, Amazon Web Services (AWS), Facebook, Baidu, Oracle, Salesforce, SAS, SAP, Others & More.

Reports include the following segmentation: By OfferingHardwareSoftwareServicesBy TechnologyMachine LearningNatural Language ProcessingContext-Aware ComputingComputer Visionby End-User IndustryHealthcareManufacturingAutomotiveAgricultureRetailSecurityHuman ResourcesMarketingLawFintechBy RegionNorth Americao U.S.o Canadao MexicoEuropeo UKo Franceo Germanyo Russiao Rest of EuropeAsia-Pacifico Chinao South Koreao Indiao Japano Rest of Asia-PacificLAMEAo Latin Americao Middle Easto Africa

Results of the recent scientific undertakings towards the development of new Artificial Intelligence (AI) products have been studied. Nevertheless, the factors affecting the leading industry players to adopt synthetic sourcing of the market products have also been studied in this statistical surveying report. The conclusions provided in this report are of great value for the leading industry players. Every organization partaking in the global production of the Artificial Intelligence (AI) market products have been mentioned in this report, in order to study the insights on cost-effective manufacturing methods, competitive landscape, and new avenues for applications.

Global Artificial Intelligence (AI)Market: Regional SegmentationFor further clarification, analysts have also segmented the market on the basis of geography. This type of segmentation allows the readers to understand the volatile political scenario in varying geographies and their impact on the global Artificial Intelligence (AI)market. On the basis of geography, the global market for Artificial Intelligence (AI)has been segmented into:

North America(United States, Canada, and Mexico)Europe(Germany, France, UK, Russia, and Italy)Asia-Pacific(China, Japan, Korea, India, and Southeast Asia)South America(Brazil, Argentina, Colombia, etc.)Middle East and Africa(Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)

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Report Methodology:

The information enclosed in this report is based upon both primary and secondary research methodologies.

Primary research methodology includes the interaction with service providers, suppliers, and industry professionals. Secondary research methodology includes a meticulous search of pertinent publications like company annual reports, financial reports, and exclusive databases.

Table of Content:

Market Overview: The report begins with this section where product overview and highlights of product and application segments of the Global Artificial Intelligence (AI) Market are provided. Highlights of the segmentation study include price, revenue, sales, sales growth rate, and market share by product.

Competition by Company: Here, the competition in the Worldwide Global Artificial Intelligence (AI) Market is analyzed, By price, revenue, sales, and market share by company, market rate, competitive situations Landscape, and latest trends, merger, expansion, acquisition, and market shares of top companies.

Company Profiles and Sales Data: As the name suggests, this section gives the sales data of key players of the Global Artificial Intelligence (AI) Market as well as some useful information on their business. It talks about the gross margin, price, revenue, products, and their specifications, type, applications, competitors, manufacturing base, and the main business of key players operating in the Global Artificial Intelligence (AI) Market.

Market Status and Outlook by Region: In this section, the report discusses about gross margin, sales, revenue, production, market share, CAGR, and market size by region. Here, the Global Artificial Intelligence (AI) Market is deeply analyzed on the basis of regions and countries such as North America, Europe, China, India, Japan, and the MEA.

Application or End User: This section of the research study shows how different end-user/application segments contribute to the Global Artificial Intelligence (AI) Market.

Market Forecast: Here, the report offers a complete forecast of the Global Artificial Intelligence (AI) Market by product, application, and region. It also offers global sales and revenue forecast for all years of the forecast period.

Research Findings and Conclusion: This is one of the last sections of the report where the findings of the analysts and the conclusion of the research study are provided.

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2025 Projeaction: Artificial Intelligence (AI) Market 2020 to 2025: Industry Scope of the Research - Cole of Duty

Recently, Black Lives Matter protests have sparked debate on social media platforms. Many have been quick with an All Lives Matter retort. Yet, in the aftermath of George Floyd and Breona Taylers deaths, there has been a pivotal need for conversations about systemic racism and the injustices black people face daily.

In fact, Google and Apple have trained artificial intelligence (AI) voice assistants to answer questions on the Black Lives Matter movement and to refute the All Lives Matter camp.

In response to Do black lives matter?, Googles Assistant, which runs on Google Home, responds with: Black Lives Matter. Black people deserve the same freedoms afforded to everyone in this country, and recognising the injustice they face is the first step towards fixing it. Furthermore, in response to Do all lives matter? Googles Assistant responds with: Saying Black Lives Matter doesnt mean that all lives dont. It means black lives are at risk in ways others are not. Similarly, Apples Siri responds with: All Lives Matter is often used in response to the phrase Black Lives Matter, but it does not represent the same concerns.

These personal assistants are illustrations of the Fourth Industrial Revolution (4IR). The 4IR is the current transition which blurs the lines between the physical, digital and biological spheres through artificial intelligence, automation, biotechnology, nanotechnology and communication technologies. Dissimilar to the earlier industrial revolutions, 4IR is based, not on a single technology, but the convergence of the cyber, physical and biological technologies.

Technologies and processes are evolving at an exponential pace and are increasingly becoming inter-related. Substantial disruptions will affect all industries and entire systems of production, management and governance and will undoubtedly transform all aspects of the 21st Century life and society. Personal assistants are primarily based on AI a technology that makes machines intelligent. A machine is considered to be intelligent if it can analyse information and extract insights beyond the obvious. Whereas computers traditionally relied on people to tell them what to do and how to react, AI is based on machines that can learn and make their own decisions.

This also works much like the patterns you learn as a human. For example, if you were to touch a hot metal object, your immediate reaction would be to pull your hand away quickly. The lesson is usually learned. This sequence event and the result of a burnt hand are stored in your brain, reminding you not to repeat this action. This knowledge means that next time you see a hot metal object; you are unlikely to touch it. This is how human intelligence works. Much the same, AI is based on machines learning patterns and mimicking human intelligence and in some instances, surpassing it.

The basic idea behind AI is to see if we can give computers some of the decision-making abilities that we as humans have. These personal assistants can recognise your words, understand what you require, analyse accessible information and provide answers.

Engineering students are probably the most equipped for this shift. The overriding advice is that people should not just stay in one lane or discipline. Crossing the road and exploring because the rapid disruptions to our society requires an integrated approach that may need people to draw on philosophy, literature, history, psychology, economics and other disciplines.

Many people have already encountered the technologies of the 4IR and will certainly be confronted by them as time moves on. Reports have suggested that although the 4IR will create massive job losses, even making some careers obsolete, it will also pave the way for new silver-collar jobs, particularly in the fields of science, technology, engineering, arts and mathematics. Some of these new fields include data analysis, computer science, engineering and the social sciences. AI will be a useful tool that people will undoubtedly deploy.

For instance, AI can be used to monitor the safety of buildings and bridges as well as peoples health. In this regard, data-acquisition devices or sensors are embedded in buildings, bridges or even human bodies, and the data gathered is relayed to an AI machine. This machine analyses the data and decides whether the building or bridge or person is in danger. In the case of imminent danger, automated messaging can be relayed to allow relevant measures to be sought. This allows for the buildings or bridges to be secured before they collapse, thereby saving lives.

AI technology has already proved to be an efficient alternative approach to classical modelling techniques. In contrast to conventional methods, AI can deal with any uncertainties that may arise and is useful in helping to solve complex problems. Ultimately, this cuts down on the tedious aspects of engineering by making the process of decision making faster, reducing error rates, and increasing efficiency. The engineer of today is vastly different from the engineer of the 19th or 20th century.

Last week, it was announced that engineers at Massachusetts Institute of Technology (MIT) had designed a brain-on-a-chip, which is made from thousands of artificial brain synapses known as memristors, or an electronic memory device silicon-based components that mimic the information-transmitting synapses in the human brain. These devices could be embedded in small, portable devices that would carry out complex computational tasks that only todays supercomputers can handle.

As Jeehwan Kim, an associate professor of mechanical engineering at MIT, explained: So far, artificial synapse networks exist as software. Were trying to build real neural network hardware for portable artificial intelligence systems. Imagine connecting a neuromorphic device to a camera on your car, and having it recognise lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.

It is becoming increasingly evident that the 4IR is fundamentally changing engineering. It is not only evolving many of the tasks involved in engineering, but it is also creating pockets of opportunity to do things that were not possible before. In fact, engineers will make up a substantial driving force of the 4IR. While there are undoubtedly fears that many jobs will be automated or made obsolete, there is room for entirely new careers and roles. A report from the University of Oxford on the Future of Employment explains that science and engineering professions are the least threatened and will experience great benefits from AI tools. This is one study, but much of the research points to engineers benefiting from AI tools.

Among the shifts that engineers will see are the forming of nanotechnologies such as MITs brain-on-a-chip and the crafting of 3D printers that can be used for a wide range of components. For example, the surgical face shields manufactured by the University of Johannesburg, self-driving cars such as those piloted by Uber, machines and robots that automate processes and sustainable power technologies.

Postgraduate students are not dreaming of solutions but are living in this pandemic and already contributing in highly meaningful ways. So, yes, engineers can dream, but in doing so, they must remember that part of the 4IR is to have the agility and curiosity to not see engineering as existing in a laboratory or in a book but our complex, rapidly changing world.

Professor Tshilidzi Marwala is the vice-chancellor and principal of the University of Johannesburg. He is the deputy chair of the Presidential Commission on the Fourth Industrial Revolution in South Africa

Continued here:

Artificial intelligence is already responding to our needs - Mail and Guardian

Global Industrial Artificial Intelligence Market OverviewGlobal Industrial Artificial Intelligence Market presents insights on the current and future industry trends, enabling the readers to identify the products and services, hence driving the revenue growth and profitability. The research report provides a detailed analysis of all the major factors impacting the market on a global and regional scale, including drivers, constraints, threats, challenges, opportunities, and industry-specific trends. Further, the report cites global certainties and endorsements along with downstream and upstream analysis of leading players.

Understand the influence of COVID-19 on the Industrial Artificial Intelligence Market Size with our analysts monitoring the situation across the globe.

The novel COVID-19 pandemic has put the world on a standstill, affecting major operations, leading to an industrial catastrophe. This report presented by Garner Insights contains a thorough analysis of the pre and post pandemic market scenarios. This report covers all the recent development and changes recorded during the COVID-19 outbreak..

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This Industrial Artificial Intelligence market report aims to provide all the participants and the vendors will all the details about growth factors, shortcomings, threats, and the profitable opportunities that the market will present in the near future. The report also features the revenue share, industry size, production volume, and consumption in order to gain insights about the politics to contest for gaining control of a large portion of the market share.

Top Key Players in the Industrial Artificial Intelligence Market: International Business Machines Corporation (US),Microsoft Corp. (US),Oracle Corp. (US),SAP SE (Germany),Salesforce.com (US),Hewlett Packard Enterprise Company (US),Alphabet Inc. (US),ServiceNow (US),CA Technology Inc. (US),Compuware Corp. (US),Fujitsu Ltd (Japan),HCL Tech (India),Red Hat (US),Wipro LTD (India),NEC Corp. (Japan),.

Competitive landscapeThe Industrial Artificial Intelligence Industry is severely competitive and fragmented due to the existence of various established players taking part in different marketing strategies to increase their market share. The vendors operating in the market are profiled based on price, quality, brand, product differentiation, and product portfolio. The vendors are turning their focus increasingly on product customization through customer interaction.Industrial Artificial Intelligence Market segment by Regions/Countries: United States, Europe, China, Japan, Southeast Asia, India, Central & South America.

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Major Types of Industrial Artificial Intelligence covered are:Hardware,Software,

Major end-user applications for Industrial Artificial Intelligence market:Semiconductor and Electronics,Energy and Power,Pharmaceuticals,Automobile,Heavy Metals and Machine Manufacturing,Food and Beverages,Others (Textiles & Aerospace),

Points Covered in The Report:

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Impact of Covid-19 on Industrial Artificial Intelligence Market is Expected to Grow at an active CAGR by Forecast to 2026 | Top Players International...