Category Archives: Data Science

Data scientists gather ‘chaos into something organized’ – University of Miami

The University of Miamis Institute for Data Science and Computing will host a lecture series to describe this emerging profession.

Data Science. It is one of the fastest growing professions in the nation, according to the U.S. Bureau of Labor Statistics. And with the sheer increase in technology able to track our every moveboth physically and digitallythe amount of information for these professionals to utilize is growing by the second.

To cultivate more of these minds in the workplace, the University of Miamis Institute for Data Science and Computing(IDSC) recently joined faculty members from across the institution to create a masters degree program that trains aspiring data scientists in four specialty tracks. And now IDSC is looking to demystify the novel profession in the lecture series Meet a Data Scientist.

This exciting new series will introduce the people behind the data, their lives, interests, and career choices, said Nick Tsinoremas, IDSC director and vice provost for research computing and data, as well as professor of biochemistry and molecular biology, computer science, and health informatics. This is a great opportunity to understand how these professionals use data to solve grand challenges in their respective fields.

Data scientists often organize massive amounts of data collected by an institution or company to find connections that could help solve problems, make decisions, or improve efficiency. For instance, if a hospital wanted to increase its productivity, a data scientist could look at the times of year when it typically has a high volume of patients, and make sure they have extra staff on hand during those weeks.

Essentially, a data scientist is someone who gathers chaos into something organized to allow others to understand it, said Alberto Cairo, the Knight Chair in Visual Communication at the School of Communication, as well as an associate professor of journalism and media management, and the director of IDSCs Center for Visualization, Data Communication, and Information Design.

In the first session of Meet a Data Scientist, Cairo described some of his experience crafting informational graphics at media companies in Brazil and Spain, as well as his current consulting role at places including Google and the Congressional Budget Office, where he takes data, structures it logically, and uses it to create visual representations of complex information in ways that are easy for the public to understand. Often, these come in the form of maps, charts, and graphs, he pointed out, but not always.

In many cases, Cairo said, he has worked with research scientists to communicate their findings more easily to a widespread audience, which helps to propel their career. He said the job of a data scientist is not only to be able to analyze the data, but to explain it in many formats.

I dont collect the data, but I help to present the data, he said.

Cairo emphasized the importance of working as a team to create the best data visualizations and mentioned that publications like The New York Times have nearly 50 data experts, including developers, graphic designers, and programmers who work together on projects. In addition, he has worked with programmers and visual artists to create Waves of Interest, an interactive illustration of the most popular Google searches during election years. And Cairo praised the work some of his former students did for The Washington Post, which localized the COVID-19 pandemic in numbers, and in The New York Times, where a survey and color coded map can help people understand the range of American dialects.

Getting your data right and your questions right is a huge amount of time spent for data scientists, he said, explaining that he often collaborates with clients extensively about their target audience and what they want to convey before getting the data and crafting ideas on how to illustrate it. I also ask scientists to explain their research to me. This helps me to organize the data, understand the relevant information, and plot it correctly, he added.

The next Meet a Data Scientist session will feature Ben Kirtman, professor of atmospheric sciences and deputy director of IDSC, as well as director of its Atmosphere, Ocean, and Earth Science division, on Wednesday, Nov. 18, from 4 to 5 p.m.

To learn more about being a data scientist, register for upcoming sessions here.

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Data scientists gather 'chaos into something organized' - University of Miami

Endowed Chair of Data Science job with Baylor University | 299439 – The Chronicle of Higher Education

The McCollum Family Endowed Chair in Data Science is aresearch-focused position in the Baylor University Computer Scienceand Informatics Department. Data Science is one of the fiveSignature Academic Initiatives in Baylors strategic planIlluminate (Illuminate- Data Science) and is involved in key research for theUniversity (Data ScienceResearch). This transformative, endowed position is a visionaryinvestment in the future of Data Science research and educationacross the university (EndowmentDetails).

Qualifications: The University invitesapplications for this tenure-track position at the rank of fullProfessor beginning in the Fall 2021 semester. An ideal candidatewill help shape a comprehensive, university-wide strategic plan forData Science. This will be done through leadership,collaboration, and growth of infrastructure and interdisciplinaryresearch. Applicants should have a Ph.D. in Data Science or arelated discipline; Baylor is recruiting new faculty with a deepcommitment to excellence in teaching, research, and scholarship.Other qualifications include an established history of extramuralfunding, high impact academic artifacts, and graduate studentmentorship. A viable applicant should demonstrate excellentpotential as an individual researcher and collaborator acrossmultiple disciplines.

The Department: Computer Science andInformatics is one of three departments in the School ofEngineering and Computer Science. It offers a B.S. in Informaticswith majors in Data Science and Bioinformatics, B.S. and B.A.degrees in Computer Science, and a B.S. in Computing with a majorin Computer Science Fellows. On location M.S. and Ph.D. degrees inComputer Science are offered, as well as an online M.S. programwhich started Fall 2020. The Department has 17 full-time faculty,over 280 undergraduates, and over 25 graduate students.Departmental website: Informatics

The University: Baylor University is a privateChristian university and a nationally ranked research institution,consistently listed with highest honors among The Chronicle ofHigher Education's "Great Colleges to Work For." Baylor seeksfaculty who share in our aspiration to become a tier-one researchinstitution while strengthening our distinctive Christian mission.As the worlds largest Baptist University, Baylor offers over 40doctoral programs and has over 17,000 students from all 50 statesand more than 85 countries.

Appointment Date: Fall 2021. For fullconsideration, applications must be received by December 31,2020.

Application Procedure: To apply, please submita letter of application, a 1-2 page research plan, a 1-2 pageteaching philosophy, a copy of an official transcript showing thehighest degree conferred (if the Ph.D. is in progress, a copy ofthe official transcript of completed Ph.D. hours should also besubmitted), and the names and email addresses of three personswilling to provide letters of recommendation as a single PDF filethrough this Interfolio link: Application Link Finalistsfor this position will be required to submit official transcriptsfor the doctoral degree in advance of a campus visit. Inquiriesabout the position can be sent toCSSearch@Baylor.edu.

Baylor University is a private not-for-profit universityaffiliated with the Baptist General Convention of Texas. As anAffirmative Action/Equal Opportunity employer, Baylor is committedto compliance with all applicable anti-discrimination laws,including those regarding age, race, color, sex, national origin,marital status, pregnancy status, military service, geneticinformation, and disability. As a religious educationalinstitution, Baylor is lawfully permitted to consider anapplicants religion as a selection criterion. Baylor encourageswomen, minorities, veterans and individuals with disabilities toapply.

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Endowed Chair of Data Science job with Baylor University | 299439 - The Chronicle of Higher Education

2020 AI and Data Science in Retail Industry Ongoing Market Situation with Manufacturing Opportunities: Amazon Web Services, Baidu Inc., BloomReach…

GlobalAI and Data Science in Retail MarketResearch Report 2019-2026:This comprehensiveAI and Data Science in Retail Marketresearch report includes a brief on these trends that can help the businesses operating in the industry to understand the market and strategize for their business expansion accordingly. The research report analyzes the market size, industry share, growth, key segments, CAGR and key drivers.

New vendors in the market are facing tough competition from established international vendors as they struggle with technological innovations, reliability and quality issues. The report will answer questions about the current market developments and the scope of competition, opportunity cost and more.

Market Overview:

The AI and Data Science in Retail market is a comprehensive report which offers a meticulous overview of the market share, size, trends, demand, product analysis, application analysis, regional outlook, competitive strategies, forecasts, and strategies impacting the AI and Data Science in Retail Industry. The report includes a detailed analysis of the market competitive landscape, with the help of detailed business profiles, SWOT analysis, project feasibility analysis, and several other details about the key companies operating in the market.

This report studies the AI and Data Science in Retail market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the AI and Data Science in Retail market by product type and applications/end industries.

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AI and Data Science in Retail Marketin its database, which provides an expert and in-depth analysis of key business trends and future market development prospects, key drivers and restraints, profiles of major market players, segmentation and forecasting. A AI and Data Science in Retail Market provides an extensive view of size; trends and shape have been developed in this report to identify factors that will exhibit a significant impact in boosting the sales of AI and Data Science in Retail Market in the near future.

Company Coverage (Sales Revenue, Price, Gross Margin, Main Products, etc.):Amazon Web Services, Baidu Inc., BloomReach Inc., CognitiveScale Inc., GoogleInc., IBM Corporation, Inbenta Technologies, IntelCorporation, Interactions LLC, Lexalytics Inc., MicrosoftCorporation, NEXT IT Corp., NvidiaCorporation, OracleCorporation, RetailNext Inc., Salesforce.com Inc., SAPSE, Sentient Technologies, Visenze.

Scope and Segmentation of the Report:

The segment analysis is one of the significant sections of this report. Our expert analyst has categorized the market into product type, application/end-user, and geography. All the segments are analyzed based on their market share, growth rate, and growth potential. In the geographical classification, the report highlights the regional markets having high growth potential. This thorough evaluation of the segments would help the players to focus on revenue-generating areas of the AI and Data Science in Retail market.

Regional Analysis:

Our analysts are experts in covering all types of geographical markets from developing to mature ones. You can expect a comprehensive research analysis of key regional and country-level markets such as Europe, North America, South America, Asia-Pacific, and the Middle East & Africa. With accurate statistical patterns and regional classification, our domain experts provide you one of the most detailed and easily understandable regional analyses of the AI and Data Science in Retail market.

Table of Contents:-

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Reports and Marketsis not just another company in this domain but is a part of a veteran group calledAlgoro Research Consultants Pvt. Ltd.It offers premium progressive statistical surveying, market research reports, analysis & forecast data for a wide range of sectors both for the government and private agencies all across the world. The database of the company is updated on a daily basis. Our database contains a variety of industry verticals that include: Food Beverage, Automotive, Chemicals and Energy, IT & Telecom, Consumer, Healthcare, and many more. Each and every report goes through the appropriate research methodology, Checked from the professionals and analysts.

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2020 AI and Data Science in Retail Industry Ongoing Market Situation with Manufacturing Opportunities: Amazon Web Services, Baidu Inc., BloomReach...

Data Science Platform Market Research Growth by Manufacturers, Regions, Type and Application, Forecast Analysis to 2026 – Eurowire

Global Data Science Platform market provides a detailed report which covers market analyses before COVID19 & opportunities after this pandemic. With COVID-19 pandemic, many industries are transforming rapidly. The Global Data Science Platform Market is one of the major industries undergoing changes. This year many industries have vanished entirely from the market and many industries have risen.

Moreover, the government-backed schemes throughout the globe are offering many advantages to businesses. As the governing bodies are supporting the industries, it be a strong pillar to support the market growth of Data Science Platform in the upcoming decade (2020-2026). Organizations planning to move into new market segments can take the help of market indicators to draw a business plan. With the technological boom, new markets are blossoming across the globe, making it a breeding ground for new businesses.

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Global Data Science Platform Market 2020: Covering both the industrial and the commercial aspects of the Global Data Science Platform Market, the report encircles several crucial chapters that give the report an extra edge. The Global Data Science Platform Market report deep dives into several parts of the report that plays a crucial role in getting the holistic view of the report. The list of such crucial aspects of the report includes company profile, industry analysis, competitive dashboard, comparative analysis of the key players, regional analysis with further analysis country wise.

Global Data Science Platform Market Analysis by Key Players:

Moreover, one of the uniqueness in the report is that it also covers the country-level analysis of the regulatory scenario, technology penetration, predictive trends, and prescriptive trends. This not only gives the readers of the report the actual real-time insights but also gives country-wise analysis, that plays a vital role in decision making. The inclusion of the report is not limited to the above mention key pointers. The report also emphasizes on the market opportunities, porters five forces, and analysis of the different types of products and application of the Global Data Science Platform Market.

The report splits by major applications:

Then report analyzed by types:

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Global Data Science Platform Market Report is a professional and in-depth research report on the worlds major regional market conditions of the Data Science Platform industry, focusing on the main regions and the main countries as Follows:

COVID-19 Impact on Data Science Platform Market:

The outbreak of COVID-19 has brought along a global recession, which has impacted several industries. Along with this impact COVID Pandemic has also generated few new business opportunities for Data Science Platform Market. Overall competitive landscape and market dynamics of Data Science Platform has been disrupted due to this pandemic. All these disruptions and impacts has been analysed quantifiably in this report, which is backed by market trends, events and revenue shift analysis. COVID impact analysis also covers strategic adjustments for Tier 1, 2 and 3 players of Data Science Platform Market.

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Table of Contents Includes Major Pointes as follows:

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Data Science Platform Market Research Growth by Manufacturers, Regions, Type and Application, Forecast Analysis to 2026 - Eurowire

Addressing the skills shortage in data science and analytics – IT-Online

Fuelled by digital expansion and the use of new-age technologies across all industries, the demand for data science skills has grown rapidly.

By Emmanuel Osanga, head: data office, Africa regions at Standard Bank Group

The supply of skilled applicants, however, is increasing at a pace that fails to match demand, and which has accelerated under the new normal.

In financial services, where significant investments are being made to enhance customer experience and engagement through new digital capabilities, more data than ever is being generated. In this context, data scientists are crucial to turning significant datasets into useful insights.

This type of future skill has been in high demand over the past few years as enterprises expand their digital footprints. Now, almost everyone has upped the ante on their digital transformation efforts in the current environment, and this means an even greater gap to be filled.

A sooner-than-expected reality

If we rewind 10 or 20 years, few would have thought about technological advancements such as Artificial Intelligence and the volume of data that could be achieved at the scale we are seeing today.

Many legacy organisations lacked planning intensity because it did not seem like a potential reality in a scenario outcome at the time. The speed at which digital advancement has taken place in recent years, and in the past few months specifically, caught many by surprise.

This left companies in a position of unpreparedness and fast-tracked the demand for the type of skills that can turn datasets into action. IBM projected in 2019 that there would be around 30% growth in demand for the data science capability in 2020 itself. This prediction is expected to escalate due to Covid-19 leading digital transformation.

While skills supply in the Science Technology Engineering and Maths has been on a heavy uptick as alternative ways of producing data scientists become available, the world is battling an under-supply and over-demand of data science human capital.

It is, however, one of the key skillsets of the future that companies will have to prepare for. But for organisations to yield appropriate quality skills, there must be investment in mastery programmes that cover the full scope of what is required to close the gap.

Data science: A multi-faceted skill

Data science is a multi-faceted skill that is not learnt overnight. Few universities, academies and online courses are bringing it into application. While they cover the theoretical aspects, it is the business domain knowledge across multiple industries that will produce the desired output.

This is a key component of what is required to be a true data scientist. No amount of technical or theoretical training in data science will solve business challenges. It is the business acumen and competence, combined with access to mentorship and on-the-ground experience, that creates the magic.

It is also about matching individuals within academies with the right type of mentality, curiosity, and inspiration and showing them what they can achieve by leveraging data. Many tend to think about it as statistics, maths, and complexity. The truth of the matter is that those who are most successful in this area are inspired to make a difference and want to solve real problems using data science.

Standard Bank data science mastery programme

Standard Banks Data Science Mastery programme, launched in 2016, fuses the fresh thinking of greenfield graduates with the competence of experienced staff members. The programme is designed not to teach theory but to provide practical experience, nurturing and mentorship.

Participants are exposed to a diverse set of problems in different fields of business areas where unique problems exist relating to that area and data-driven type solutions differ.

The ability to combine technical or theory with practical experience by working alongside internal employees is proving invaluable. The experience of rotating individuals across business areas cannot be replaced.

Standard Bank has worked to improve the overall data literacy of the organisation to support its activities now and in the future. When exposed to the skill and its possibilities, individuals become interested in mastering data language.

This is expected to place further demand on the programme, which has already received an overwhelming response. It has, however, been structured to scale over the next two to three years to meet the demand both internally and externally.

This investment is one of the significant strategic decisions that we, as Standard Bank, have made to build a foundation for the platform journey we are now on. Our extended agreement with CRM Salesforce is a major step towards transforming the Standard Bank Group into a client-centred platform business that delivers a range of individualised, instantly available solutions, services and opportunities, enabled by modern digital technologies and delivered in whatever way a client prefers.

The Salesforce investment requires every staff member to reflect on their current skill set, and whether it represents that of the future. If not, there is an expectation that everyone undergoes reskilling to cope and fundamentally evolve and adapt.

Africa can accelerate the solve

There is a massive opportunity for Africa as a continent, given its young population, to skill its people appropriately for the future. There is great demand for the data science capability yet significant unemployment plagues the continent. Africas youth are, however, ripe for these capabilities; they are more inclined to quickly comprehend technology because that is their immediate experience.

The 4IR presents a golden opportunity for Africa. The rapid evolution of digital technology has caught the world off guard. Legacy organisations in well-established economies are undergoing transformation processes to digitally adopt. The continent, meanwhile, is a blank canvas unhindered by legacy that can leapfrog the worlds delay into the adoption if connectivity is enabled.

Investing in a data science mastery programme reinforces Standard Banks commitment to driving Africas growth. We are preparing Africans to be relevant in the future. Africa is our home, we are contributing to the future of the continent by laying these foundations. We will continue to expand on that and be part of the African story of preparing to meet demand for future skills in the region and globally as well.

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Addressing the skills shortage in data science and analytics - IT-Online

UTSA to break ground on $90 million School of Data Science and National Security Collaboration Center – Construction Review

The University of Texas at San Antonio will commence construction of a $90 million School of Data Science and National Security Collaboration Center in the coming few days. The 167,000-square-foot building is located along Dolorosa Street east of Interstate 35 and is a major component of the universitys downtown campus expansion plan. The project is expected to spur business in the area and attract private investment.

Corrina Green, director of major capital projects and real estate for UTSA while giving an update on the proposed School of Data Science and National Security Collaboration Center said they have already finished developing the project design.

UTSA officials are awaiting design approvals from the UT System Board of Regents before commencing construction in mid-December. The project is expected to be complete by July 2022.

The building design has tried to draw more attention to a reimagined San Pedro Creek. The centers ground level will be reserved for a caf plus a large mixed-use space that will be used by students and tenants and also available for public events.

We really want to draw people into the building and up from the creek so that we are interacting with the other development thats happening around us, Green said. Were working hand in hand with all of these developers to make sure that this is very engaging with what they are doing.

UTSA also announced plans to construct a 250,000-square-foot Innovation, Entrepreneurship, and Careers Building at the site where the countys old prison is being demolished. The building will start west of the new structure that is being constructed.

The University is yet to release the timeline for the Innovation, Entrepreneurship, and Careers Building whose construction is expected to cost $161.2 million. This building will house an expanded College business.

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UTSA to break ground on $90 million School of Data Science and National Security Collaboration Center - Construction Review

Risks and benefits of an AI revolution in medicine – Harvard Gazette

If you start applying it, and its wrong, and we have no ability to see that its wrong and to fix it, you can cause more harm than good, Jha said. The more confident we get in technology, the more important it is to understand when humans can override these things. I think the Boeing 737 Max example is a classic example. The system said the plane is going up, and the pilots saw it was going down but couldnt override it.

Jha said a similar scenario could play out in the developing world should, for example, a community health worker see something that makes him or her disagree with a recommendation made by a big-name companys AI-driven app. In such a situation, being able to understand how the apps decision was made and how to override it is essential.

If you see a frontline community health worker in India disagree with a tool developed by a big company in Silicon Valley, Silicon Valley is going to win, Jha said. And thats potentially a dangerous thing.

Researchers at SEAS and MGHs Radiology Laboratory of Medical Imaging and Computation are at work on the two problems. The AI-based diagnostic system to detect intracranial hemorrhages unveiled in December 2019 was designed to be trained on hundreds, rather than thousands, of CT scans. The more manageable number makes it easier to ensure the data is of high quality, according to Hyunkwang Lee, a SEAS doctoral student who worked on the project with colleagues including Sehyo Yune, a former postdoctoral research fellow at MGH Radiology and co-first author of a paper on the work, and Synho Do, senior author, HMS assistant professor of radiology, and director of the lab.

We ensured the data set is of high quality, enabling the AI system to achieve a performance similar to that of radiologists, Lee said.

Second, Lee and colleagues figured out a way to provide a window into an AIs decision-making, cracking open the black box. The system was designed to show a set of reference images most similar to the CT scan it analyzed, allowing a human doctor to review and check the reasoning.

Jonathan Zittrain, Harvards George Bemis Professor of Law and director of the Berkman Klein Center for Internet and Society, said that, done wrong, AI in health care could be analogous to the cancer-causing asbestos that was used for decades in buildings across the U.S., with widespread harmful effects not immediately apparent. Zittrain pointed out that image analysis software, while potentially useful in medicine, is also easily fooled. By changing a few pixels of an image of a cat still clearly a cat to human eyes MIT students prompted Google image software to identify it, with 100 percent certainty, as guacamole. Further, a well-known study by researchers at MIT and Stanford showed that three commercial facial-recognition programs had both gender and skin-type biases.

Ezekiel Emanuel, a professor of medical ethics and health policy at the University of Pennsylvanias Perelman School of Medicine and author of a recent Viewpoint article in the Journal of the American Medical Association, argued that those anticipating an AI-driven health care transformation are likely to be disappointed. Though he acknowledged that AI will likely be a useful tool, he said it wont address the biggest problem: human behavior. Though they know better, people fail to exercise and eat right, and continue to smoke and drink too much. Behavior issues also apply to those working within the health care system, where mistakes are routine.

We need fundamental behavior change on the part of these people. Thats why everyone is frustrated: Behavior change is hard, Emanuel said.

Susan Murphy, professor of statistics and of computer science, agrees and is trying to do something about it. Shes focusing her efforts on AI-driven mobile apps with the aim of reinforcing healthy behaviors for people who are recovering from addiction or dealing with weight issues, diabetes, smoking, or high blood pressure, conditions for which the personal challenge persists day by day, hour by hour.

The sensors included in ordinary smartphones, augmented by data from personal fitness devices such as the ubiquitous Fitbit, have the potential to give a well-designed algorithm ample information to take on the role of a health care angel on your shoulder.

The tricky part, Murphy said, is to truly personalize the reminders. A big part of that, she said, is understanding how and when to nudge not during a meeting, for example, or when youre driving a car, or even when youre already exercising, so as to best support adopting healthy behaviors.

How can we provide support for you in a way that doesnt bother you so much that youre not open to help in the future? Murphy said. What our algorithms do is they watch how responsive you are to a suggestion. If theres a reduction in responsivity, they back off and come back later.

The apps can use sensors on your smartphone to figure out whats going on around you. An app may know youre in a meeting from your calendar, or talking more informally from ambient noise its microphone detects. It can tell from the phones GPS how far you are from a gym or an AA meeting or whether you are driving and so should be left alone.

Trickier still, Murphy said, is how to handle moments when the AI knows more about you than you do. Heart rate sensors and a phones microphone might tell an AI that youre stressed out when your goal is to live more calmly. You, however, are focused on an argument youre having, not its physiological effects and your long-term goals. Does the app send a nudge, given that its equally possible that you would take a calming breath or angrily toss your phone across the room?

Working out such details is difficult, albeit key, Murphy said, in order to design algorithms that are truly helpful, that know you well, but are only as intrusive as is welcome, and that, in the end, help you achieve your goals.

For AI to achieve its promise in health care, algorithms and their designers have to understand the potential pitfalls. To avoid them, Kohane said its critical that AIs are tested under real-world circumstances before wide release.

Similarly, Jha said its important that such systems arent just released and forgotten. They should be reevaluated periodically to ensure theyre functioning as expected, which would allow for faulty AIs to be fixed or halted altogether.

Several experts said that drawing from other disciplines in particular ethics and philosophy may also help.

Programs like Embedded EthiCS at SEAS and the Harvard Philosophy Department, which provides ethics training to the Universitys computer science students, seek to provide those who will write tomorrows algorithms with an ethical and philosophical foundation that will help them recognize bias in society and themselves and teach them how to avoid it in their work.

Disciplines dealing with human behavior sociology, psychology, behavioral economics not to mention experts on policy, government regulation, and computer security, may also offer important insights.

The place were likely to fall down is the way in which recommendations are delivered, Bates said. If theyre not delivered in a robust way, providers will ignore them. Its very important to work with human factor specialists and systems engineers about the way that suggestions are made to patients.

Bringing these fields together to better understand how AIs work once theyre in the wild is the mission of what Parkes sees as a new discipline of machine behavior. Computer scientists and health care experts should seek lessons from sociologists, psychologists, and cognitive behaviorists in answering questions about whether an AI-driven system is working as planned, he said.

How useful was it that the AI system proposed that this medical expert should talk to this other medical expert? Parkes said. Was that intervention followed? Was it a productive conversation? Would they have talked anyway? Is there any way to tell?

Next: A Harvard project asks people to envision how technology will change their lives going forward.

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Risks and benefits of an AI revolution in medicine - Harvard Gazette

IPG Unveils New-And-Improved Copy For Data: It’s Not Your Father’s ‘Targeting’ 11/11/2020 – MediaPost Communications

EASTON, CT -- Data scientists may be the new rock stars ofMadison Avenue, but there's a reason they don't write copy for ads and Arun Kumar, Chief Data & Marketing Technology Officer of IPG and CEO of its Kinesso unit, proved it Tuesday when he presentedrecommendations for the ad industry's new language for consumer "targeting" during the day's opening keynote at i-com's Global Data Summit online.

Instead of the word "targeting," Kumar saidIPG is advocating words like "reach," "reaching, "connect," "connecting," "addressable," and "personalized."

Instead of a term like "recognizing actual people," Kumar said IPG recommends"identifying customers consistently as the same person across multiple channels and touch points."

If that copy sounds a bit squishy, it's intentional, because Kumar said IPG and otherindustry research shows most consumers, regulators and other key stakeholders have become acutely concerned about the power of marketing data science to identify and target people, and the new, softerlanguage is a way of assuaging those concerns. Ironically, it also seems like classic Madison Avenue' copysmithing intended to obscure, redirect and create less transparency, not more.

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ButKumar acknowledges that the marketing industry is between a "rock and a hard place," and the new-and-improved language is intended to lower the temperature surrounding the broader debate so thatMadison Avenue can focus on the benefits that responsible use of data has for consumers and society at large.

Kumar cited research conducted by IPG's Acxiom unit indicating that when askedabout their data-privacy concerns, most consumers are not concerned about being targeted per se, but that they are actually worried about potential "identity theft," and that there has been "a lot ofconflation between many of those terms and marketing."

That helps explain IPG's recommended new language, which grew out of broader "digital responsibility program" launched by IPG, which hasmade the ethical use of consumer data a core focus of its organization.

During his presentation, Kumar provided examples of data-based marketing campaigns in the telecommunications andautomotive category that generated improved "lifts" utilizing privacy compliant techniques developed by the agency. He said they achieved these improvements by utilizing safe identity resolutiontechniques that reduced "customer churn" by making ads more relevant and "stickier with our products and services."

In the end, Kumar told the i-com data marketing attendees that they shouldthink about what they do the way consumers would.

"If you are a consumer, think of the number of times that you've had an experience and shook your head and said, "That branddoesnt know me at all. Ive just bought that product and theyre still chasing me with ads.'"

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IPG Unveils New-And-Improved Copy For Data: It's Not Your Father's 'Targeting' 11/11/2020 - MediaPost Communications

Data Science and Machine-Learning Platforms Market Size, Drivers, Potential Growth Opportunities, Competitive Landscape, Trends And Forecast To 2027 -…

Data Science and Machine-Learning Platforms Market Overview

The Global Data Science and Machine-Learning Platforms Market is showing positive signs of growth. With the current COVID-19 pandemic scenario, new business opportunities are sprouting in the market. Organizations must explore new markets to expand their business globally and locally. For getting a deeper understanding of the emerging trends, the Global Data Science and Machine-Learning Platforms Market report showcases various factors that drive the economy worldwide. Moreover, the companies will get to know the market landscape for the next decade 2020-2027.

The Global Data Science and Machine-Learning Platforms Market report has been uniquely designed to cater to the needs of the businesses of the 21st century. Going digital is the new normal. Moreover, companies can get to understand their strengths and weaknesses after assessing the market. The next decade is going to be ruled by customer-centric services. To align the business operations, the management team can utilize the actionable recommendations offered at the end of the Global Data Science and Machine-Learning Platforms Market report. Factors that can lift or reduce the business are termed as the external factors that also govern the functioning of the market or industries as a whole.

Before designing the blueprint, every business group can go through the Global Data Science and Machine-Learning Platforms Market report to understand the key business areas. For shaping a new business venture or expanding into a new market, every company must look into the opportunities and threats that are lurking in the current market. To make an efficient business plan, corporations need to understand the market dynamics that will shape the market in the forecast period (2020-2027).

Following key players have been profiled with the help of proven research methodologies:

Data Science and Machine-Learning Platforms Market: Competitive Landscape

To get a head start in a new market, every enterprise needs to understand the competitive landscape and the basic rules that have kept the specific market afloat. The global Data Science and Machine-Learning Platforms Market report unravels the secret ingredients used by competitors to meet the demands of their target audience. For specifically understanding the need to balance the capital invested with profits, organizations must use specific indicators. These indicators will not only help in pointing towards growth but also act as an alert to the upcoming threats in the near future. A proper business plan and approach can guarantee a smooth path ahead for every organization.

If the firms believe in offering a memorable experience to their prospective customers, the Global Data Science and Machine-Learning Platforms Market report is going to be very useful. Facts and figures are churned into this investigative report to share the strengths and weaknesses of the company. With new technologies being introduced every day, many new entrants have started their business in the market. So, to understand their approach towards the market, the Global Data Science and Machine-Learning Platforms Market report has a dedicated section. From the financial aspect to legal, the market report covers all the major things required to study the market and put the business plan in action. Not only this, the competitors added in the report can be altered as per the clients needs and expectations. Furthermore, the companies get a basic outline of moves, in the Global Data Science and Machine-Learning Platforms Market report, that can push the business to emerald heights, both in terms of sales and customer generation for the estimated time frame (2020-2027).

Data Science and Machine-Learning Platforms Market Segmentation:

The Data Science and Machine-Learning Platforms Market has been examined into different global market segments such as type, applications, and global geographies. Each and every global market segment has been studied to get informative insights into various global regions.

Data Science and Machine-Learning Platforms Market Segment by Type:

Data Science and Machine-Learning Platforms Market Segment by Application:

Data Science and Machine-Learning Platforms Market Segment by Global Presence:

North America Latin America Middle East Asia-Pacific Africa Europe

The report has been aggregated by using a couple of research methodologies such as primary and secondary research techniques. It helps in collecting informative pieces of professional information for deriving effective insights into the market. This informative report helps in making well informed and strategic decisions throughout the forecast period.

Data Science and Machine-Learning Platforms Market: Scope of the Report

To properly get a deeper understanding of the Global Data Science and Machine-Learning Platforms Market, this detailed report is the best choice for businesses. To boost the business along with gaining an edge over the competition, every enterprise needs to focus on the pain points of the market (under investigation). Our experienced professionals have collated facts and figures.

This in-depth analysis has revealed many fascinating facts for organizations. For smooth functioning, every business needs to be flexible towards the latest market trends. For this, the framework must be designed to adapt to the trends running at the moment. An end-to-end examination done on the target crowd helped in building the fundamental segment of the investigation, namely the external factors. These have a high tendency to push or pull the industries. Entire industries can either flourish or wipe out due to these uncontrollable factors. Global Data Science and Machine-Learning Platforms market report shows the most affordable options for new as well as established business players to gain market share.

With the highly experienced and motivated team at your service, the team also provides the impact of major factors such as Porters five forces. In the Global Data Science and Machine-Learning Platforms Market, every business runs on the image that is generated digitally in the current decade. Hence, companies need to understand the legal hurdles also. Moreover, with the in-depth study conducted across the various market verticals, it is crystal clear that stakeholders also play a significant role in running the business. Get all the details in the Global Data Science and Machine-Learning Platforms Market report and understand your competitors.

Key questions answered through this analytical market research report include:

What are the latest trends, new patterns and technological advancements in the Data Science and Machine-Learning Platforms Market? Which factors are influencing the Data Science and Machine-Learning Platforms Market over the forecast period? What are the global challenges, threats and risks in the Data Science and Machine-Learning Platforms Market? Which factors are propelling and restraining the Data Science and Machine-Learning Platforms Market? What are the demanding global regions of the Data Science and Machine-Learning Platforms Market? What will be the global market size over the coming future? What are the different effective business strategies followed by global companies?

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Data Science and Machine-Learning Platforms Market Size, Drivers, Potential Growth Opportunities, Competitive Landscape, Trends And Forecast To 2027 -...

Industrial Access Control Market 2020-28 use of data science in agriculture to maximize yields and efficiency with top key players – TechnoWeekly

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Industrial Access Control Market 2020-28 use of data science in agriculture to maximize yields and efficiency with top key players - TechnoWeekly