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Verified Market Research offers its latest report on the Machine Learning Market that includes a comprehensive analysis of a range of subjects such as market opportunities, competition, segmentation, regional expansion, and market dynamics. It prepares players also as investors to require competent decisions and plan for growth beforehand. This report is predicted to assist the reader understand the market with reference to its various drivers, restraints, trends, and opportunities to equip them in making careful business decisions.

Global Machine Learning Market was valued at USD 2.03 Billion in 2018 and is projected to reach USD 37.43 Billion by 2026, growing at a CAGR of 43.9% from 2019 to 2026.

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The top manufacturer with company profile, sales volume, and product specifications, revenue (Million USD) and market share

Global Machine Learning Market: Competitive Landscape

The chapter on competitive landscape covers all the major manufacturers in the global Smart Cameramarket to study new trends and opportunities. In this section, the researchers have used SWOT analysis to study the various strengths, weaknesses, opportunities, and trends the manufacturers are using to expand their share. Furthermore, they have briefed about the trends that are expected to drive the market in the future and open more opportunities.

Global Machine Learning Market: Drivers and Restraints

The researchers have analyzed various factors that are necessary for the growth of the market in global terms. They have taken different perspectives for the market including technological, social, political, economic, environmental, and others. The drivers have been derived using PESTELs analysis to keep them accurate. Factors responsible for propelling the growth of the market and helping its growth in terms of market share are been studied objectively.

Furthermore, restraints present in the market have been put together using the same process. Analysts have provided a thorough assessment of factors likely to hold the market back and offered solutions for circumventing the same too.

Global Machine Learning Market: Segment Analysis

The researchers have segmented the market into various product types and their applications. This segmentation is expected to help the reader understand where the market is observing more growth and which product and application hold the largest share in the market. This will give them leverage over others and help them invest wisely.

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Machine Learning Market: Regional Analysis :

As part of regional analysis, important regions such as North America, Europe, the MEA, Latin America, and Asia Pacific have been studied. The regional Machine Learning markets are analyzed based on share, growth rate, size, production, consumption, revenue, sales, and other crucial factors. The report also provides country-level analysis of the Machine Learning industry.

Table of Contents

Introduction: The report starts off with an executive summary, including top highlights of the research study on the Machine Learning industry.

Market Segmentation: This section provides detailed analysis of type and application segments of the Machine Learning industry and shows the progress of each segment with the help of easy-to-understand statistics and graphical presentations.

Regional Analysis: All major regions and countries are covered in the report on the Machine Learning industry.

Market Dynamics: The report offers deep insights into the dynamics of the Machine Learning industry, including challenges, restraints, trends, opportunities, and drivers.

Competition: Here, the report provides company profiling of leading players competing in the Machine Learning industry.

Forecasts: This section is filled with global and regional forecasts, CAGR and size estimations for the Machine Learning industry and its segments, and production, revenue, consumption, sales, and other forecasts.

Recommendations: The authors of the report have provided practical suggestions and reliable recommendations to help players to achieve a position of strength in the Machine Learning industry.

Research Methodology: The report provides clear information on the research approach, tools, and methodology and data sources used for the research study on the Machine Learning industry.

Complete Report is Available @ https://www.verifiedmarketresearch.com/product/global-machine-learning-market-size-and-forecast-to-2026/?utm_source=ITN&utm_medium=003

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Machine Learning Market Booming by Size, Revenue, Trends and Top Growing Companies 2026 - Instant Tech News

Retailers are harnessing the power of new technology to dig deeper into customer decisions and bring people back into stores.

The National Retail Federation's 2020 Big Show in New York was jam packed full of robots, frictionless store mock-ups, and audacious displays of the latest technology now available to retailers.

Dozens of robots, digital signage tools, and more were available for retail representatives to test out, with hundreds of the biggest tech companies in attendance offering a bounty of eye-popping gadgets designed to increase efficiency and bring the wow factor back to brick-and-mortar stores.

SEE: Artificial intelligence: A business leader's guide (free PDF) (TechRepublic)

Here are some of the biggest takeaways from the annual retail event.

With the explosion in popularity of Amazon, Alibaba, and other e-commerce sites ready to deliver goods right to your door within days, many analysts and retailers figured the brick-and-mortar stores of the past were on their last legs.

But it turns out billions of customers still want the personal, tailored touch of in-store experiences and are not ready to completely abandon physical retail outlets.

"It's not a retail apocalypse. It's a retail renaissance," said Lori Mitchell-Keller, executive vice president and global general manager of consumer industries at SAP.

As leader of SAP's retail, wholesale distribution, consumer products, and life sciences industries division, Mitchell-Keller said she was surprised to see that retailers had shifted their stance and were looking to find ways to beef up their online experience while infusing stores with useful but flashy technology.

"Brick-and-mortar stores have this unique capability to have a specific advantage against online retailers. So despite the trend where everything was going online, it did not mean online at the expense of brick-and-mortar. There is a balance between the two. Those companies that have a great online experience and capability combined with a brick-and-mortar store are in the best place in terms of their ability to be profitable," Mitchell-Keller said during an interview at NRF 2020.

"There is an experience that you cannot get online. This whole idea of customer experience and experience management is definitely the best battleground for the guys that can't compete in delivery. Even for the ones that can compete on delivery, like the Walmarts and Targets, they are using their brick-and-mortar stores to offer an experience that you can't get online. We thought five years ago that brick-and-mortar was dead and it's absolutely not dead. It's actually an asset."

In her experience working with the world's biggest retailers, companies that have a physical presence actually have a huge advantage because customers are now yearning for a personalized experience they can't get online. While e-commerce sites are fast, nothing can beat the ability to have real people answer questions and help customers work through their options, regardless of what they're shopping for.

Retailers are also transforming parts of their stores into fulfillment centers for their online sales, which have the doubling effect of bringing customers into the store where they may spend even more on things they see.

"The brick-and-mortar stores that are using their stores as fulfillment centers have a much lower cost of delivery because they're typically within a few miles of customers. If they have a great online capability and good store fulfillment, they're able to get to customers faster than the aggregators," Mitchell-Keller said. "It's better to have both."

SEE: Feature comparison: E-commerce services and software (TechRepublic Premium)

But one of the main trends, and problems, highlighted at NRF 2020 was the sometimes difficult transition many retailers have had to make to a digitized world.

NRF 2020 was full of decadent tech retail tools like digital price tags, shelf-stocking robots and next-gen advertising signage, but none of this could be incorporated into a retail environment without a basic amount tech talent and systems to back it all.

"It can be very overwhelmingly complicated, not to mention costly, just to have a team to manage technology and an environment that is highly digitally integrated. The solution we try to bring to bear is to add all these capabilities or applications into a turn key environment because fundamentally, none of it works without the network," said Michael Colaneri, AT&T's vice president of retail, restaurants and hospitality.

While it would be easy for a retailer to leave NRF 2020 with a fancy robot or cool gadget, companies typically have to think bigger about the changes they want to see, and generally these kinds of digital transformations have to be embedded deep throughout the supply chain before they can be incorporated into stores themselves.

Colaneri said much of AT&T's work involved figuring out how retailers could connect the store system, the enterprise, the supply chain and then the consumer, to both online and offline systems. The e-commerce part of retailer's business now had to work hand in hand with the functionality of the brick-and-mortar experience because each part rides on top of the network.

"There are five things that retailers ask me to solve: Customer experience, inventory visibility, supply chain efficiency, analytics, and the integration of media experiences like a robot, electronic shelves or digital price tags. How do I pull all this together into a unified experience that is streamlined for customers?" Colaneri said.

"Sometimes they talk to me about technical components, but our number one priority is inventory visibility. I want to track products from raw material to where it is in the legacy retail environment. Retailers also want more data and analytics so they can get some business intelligence out of the disparate data lakes they now have."

The transition to digitized environments is different for every retailer, Colaneri added. Some want slow transitions and gradual introductions of technology while others are desperate for a leg up on the competition and are interested in quick makeovers.

While some retailers have balked at the thought, and price, of wholesale changes, the opposite approach can end up being just as costly.

"Anybody that sells you a digital sign, robot, Magic Mirror or any one of those assets is usually partnering with network providers because it requires the network. And more importantly, what typically happens is if someone buys an asset, they are underestimating the requirements it's going to need from their current network," Colaneri said.

"Then when their team says 'we're already out of bandwidth,' you'll realize it wasn't engineered and that the application wasn't accommodated. It's not going to work. It can turn into a big food fight."

Retailers are increasingly realizing the value of artificial intelligence and machine learning as a way to churn through troves of data collected from customers through e-commerce sites. While these tools require the kind of digital base that both Mitchell-Keller and Colaneri mentioned, artificial intelligence (AI) and machine learning can be used to address a lot of the pain points retailers are now struggling with.

Mitchell-Keller spoke of SAP's work with Costco as an example of the kind of real-world value AI and machine learning can add to a business. Costco needed help reducing waste in their bakeries and wanted better visibility into when customers were going to buy particular products on specific days or at specific times.

"Using machine learning, what SAP did was take four years of data out of five different stores for Costco as a pilot and used AI and machine learning to look through the data for patterns to be able to better improve their forecasting. They're driving all of their bakery needs based on the forecast and that forcecast helped Costco so much they were able to reduce their waste by about 30%," Mitchell-Keller said, adding that their program improved productivity by 10%.

SAP and dozens of other tech companies at NRF 2020 offered AI-based systems for a variety of supply chain management tools, employee payment systems and even resume matches. But AI and machine learning systems are nothing without more data.

SEE:Managing AI and ML in the enterprise 2019: Tech leaders expect more difficulty than previous IT projects(TechRepublic Premium)

Jeff Warren, vice president of Oracle Retail, said there has been a massive shift toward better understanding customers through increased data collection. Historically, retailers simply focused on getting products through the supply chain and into the hands of consumers. But now, retailers are pivoting toward focusing on how to better cater services and goods to the customer.

Warren said Oracle Retail works with about 6,000 retailers in 96 different countries and that much of their work now prioritizes collecting information from every customer interaction.

"What is new is that when you think of the journey of the consumer, it's not just about selling anymore. It's not just about ringing up a transaction or line busting. All of the interactions between you and me have value and hold something meaningful from a data perspective," he said, adding that retailers are seeking to break down silos and pool their data into a single platform for greater ease of use.

"Context would help retailers deliver a better experience to you. Its petabytes of information about what the US consumer market is spending and where they're spending. We can take the information that we get from those interactions that are happening at the point of sale about our best customers and learn more."

With the Oracle platform, retailers can learn about their customers and others who may have similar interests or live in similar places. Companies can do a better job of targeting new customers when they know more about their current customers and what else they may want.

IBM is working on similar projects with hundreds of different retailers , all looking to learn more about their customers and tailor their e-commerce as well as in-store experience to suit their biggest fans.

IBM global managing director for consumer industries Luq Niazi told TechRepublic during a booth tour that learning about consumer interests was just one aspect of how retailers could appeal to customers in the digital age.

"Retailers are struggling to work through what tech they need. When there is so much tech choice, how do you decide what's important? Many companies are implementing tech that is good but implemented badly, so how do you help them do good tech implemented well?" Niazi said.

"You have all this old tech in stores and you have all of this new tech. You have to think about how you bring the capability together in the right way to deploy flexibly whatever apps and experiences you need from your store associate, for your point of sale, for your order management system that is connected physically and digitally. You've got to bring those together in different ways. We have to help people think about how they design the store of the future."

Get expert tips on mastering the fundamentals of big data analytics, and keep up with the latest developments in artificial intelligence. Delivered Mondays

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AI, machine learning, robots, and marketing tech coming to a store near you - TechRepublic

In a major operators network control center complaints are flooding in. The network is down across a large US city; calls are getting dropped and critical infrastructure is slow to respond. Pulling up the systems event history, the manager sees that new 5G towers were installed in the affected area today.

Did installing those towers cause the outage, or was it merely a coincidence? In circumstances such as these, being able to answer this question accurately is crucial for Ericsson.

Most machine learning-based data science focuses on predicting outcomes, not understanding causality. However, some of the biggest names in the field agree its important to start incorporating causality into our AI and machine learning systems.

Yoshua Bengio, one of the worlds most highly recognized AI experts, explained in a recent Wired interview: Its a big thing to integrate [causality] into AI. Current approaches to machine learning assume that the trained AI system will be applied on the same kind of data as the training data. In real life it is often not the case.

Yann LeCun, a recent Turing Award winner, shares the same view, tweeting: Lots of people in ML/DL [deep learning] know that causal inference is an important way to improve generalization.

Causal inference and machine learning can address one of the biggest problems facing machine learning today that a lot of real-world data is not generated in the same way as the data that we use to train AI models. This means that machine learning models often arent robust enough to handle changes in the input data type, and cant always generalize well. By contrast, causal inference explicitly overcomes this problem by considering what might have happened when faced with a lack of information. Ultimately, this means we can utilize causal inference to make our ML models more robust and generalizable.

When humans rationalize the world, we often think in terms of cause and effect if we understand why something happened, we can change our behavior to improve future outcomes. Causal inference is a statistical tool that enables our AI and machine learning algorithms to reason in similar ways.

Lets say were looking at data from a network of servers. Were interested in understanding how changes in our network settings affect latency, so we use causal inference to proactively choose our settings based on this knowledge.

The gold standard for inferring causal effects is randomized controlled trials (RCTs) or A/B tests. In RCTs, we can split a population of individuals into two groups: treatment and control, administering treatment to one group and nothing (or a placebo) to the other and measuring the outcome of both groups. Assuming that the treatment and control groups arent too dissimilar, we can infer whether the treatment was effective based on the difference in outcome between the two groups.

However, we can't always run such experiments. Flooding half of our servers with lots of requests might be a great way to find out how response time is affected, but if theyre mission-critical servers, we cant go around performing DDOS attacks on them. Instead, we rely on observational datastudying the differences between servers that naturally get a lot of requests and those with very few requests.

There are many ways of answering this question. One of the most popular approaches is Judea Pearl's technique for using to statistics to make causal inferences. In this approach, wed take a model or graph that includes measurable variables that can affect one another, as shown below.

To use this graph, we must assume the Causal Markov Condition. Formally, it says that subject to the set of all its direct causes, a node is independent of all the variables which are not direct causes or direct effects of that node. Simply put, it is the assumption that this graph captures all the real relationships between the variables.

Another popular method for inferring causes from observational data is Donald Rubin's potential outcomes framework. This method does not explicitly rely on a causal graph, but still assumes a lot about the data, for example, that there are no additional causes besides the ones we are considering.

For simplicity, our data contains three variables: a treatment , an outcome , and a covariate . We want to know if having a high number of server requests affects the response time of a server.

In our example, the number of server requests is determined by the memory value: a higher memory usage means the server is less likely to get fed requests. More precisely, the probability of having a high number of requests is equal to 1 minus the memory value (i.e. P(x=1)=1-z , where P(x=1) is the probability that x is equal to 1). The response time of our system is determined by the equation (or hypothetical model):

y=1x+5z+

Where is the error, that is, the deviation from the expected value of given values of and depends on other factors not included in the model. Our goal is to understand the effect of on via observations of the memory value, number of requests, and response times of a number of servers with no access to this equation.

There are two possible assignments (treatment and control) and an outcome. Given a random group of subjects and a treatment, each subject has a pair of potential outcomes: and , the outcomes Y_i (0) and Y_i (1) under control and treatment respectively. However, only one outcome is observed for each subject, the outcome under the actual treatment received: Y_i=xY_i (1)+(1-x)Y_i (0). The opposite potential outcome is unobserved for each subject and is therefore referred to as a counterfactual.

For each subject, the effect of treatment is defined to be Y_i (1)-Y_i (0) . The average treatment effect (ATE) is defined as the average difference in outcomes between the treatment and control groups:

E[Y_i (1)-Y_i (0)]

Here, denotes an expectation over values of Y_i (1)-Y_i (0)for each subject , which is the average value across all subjects. In our network example, a correct estimate of the average treatment effect would lead us to the coefficient in front of x in equation (1) .

If we try to estimate this by directly subtracting the average response time of servers with x=0 from the average response time of our hypothetical servers with x=1, we get an estimate of the ATE as 0.177 . This happens because our treatment and control groups are not inherently directly comparable. In an RTC, we know that the two groups are similar because we chose them ourselves. When we have only observational data, the other variables (such as the memory value in our case) may affect whether or not one unit is placed in the treatment or control group. We need to account for this difference in the memory value between the treatment and control groups before estimating the ATE.

One way to correct this bias is to compare individual units in the treatment and control groups with similar covariates. In other words, we want to match subjects that are equally likely to receive treatment.

The propensity score ei for subject is defined as:

e_i=P(x=1z=z_i ),z_i[0,1]

or the probability that x is equal to 1the unit receives treatmentgiven that we know its covariate is equal to the value z_i. Creating matches based on the probability that a subject will receive treatment is called propensity score matching. To find the propensity score of a subject, we need to predict how likely the subject is to receive treatment based on their covariates.

The most common way to calculate propensity scores is through logistic regression:

Now that we have calculated propensity scores for each subject, we can do basic matching on the propensity score and calculate the ATE exactly as before. Running propensity score matching on the example network data gets us an estimate of 1.008 !

We were interested in understanding the causal effect of binary treatment x variable on outcome y . If we find that the ATE is positive, this means an increase in x results in an increase in y. Similarly, a negative ATE says that an increase in x will result in a decrease in y .

This could help us understand the root cause of an issue or build more robust machine learning models. Causal inference gives us tools to understand what it means for some variables to affect others. In the future, we could use causal inference models to address a wider scope of problems both in and out of telecommunications so that our models of the world become more intelligent.

Special thanks to the other team members of GAIA working on causality analysis: Wenting Sun, Nikita Butakov, Paul Mclachlan, Fuyu Zou, Chenhua Shi, Lule Yu and Sheyda Kiani Mehr.

If youre interested in advancing this field with us, join our worldwide team of data scientists and AI specialists at GAIA.

In this Wired article, Turing Award winner Yoshua Bengio shares why deep learning must begin to understand the why before it can replicate true human intelligence.

In this technical overview of causal inference in statistics, find out whats needed to evolve AI from traditional statistical analysis to causal analysis of multivariate data.

This journal essay from 1999 offers an introduction to the Causal Markov Condition.

Go here to read the rest:
Overview of causal inference in machine learning - Ericsson

With the coronavirus growing more deadly in China, artificial intelligence researchers are applying machine-learning techniques to social media, web, and other data for subtle signs that the disease may be spreading elsewhere.

The new virus emerged in Wuhan, China, in December, triggering a global health emergency. It remains uncertain how deadly or contagious the virus is, and how widely it might have already spread. Infections and deaths continue to rise. More than 31,000 people have now contracted the disease in China, and 630 people have died, according to figures released by authorities there Friday.

John Brownstein, chief innovation officer at Harvard Medical School and an expert on mining social media information for health trends, is part of an international team using machine learning to comb through social media posts, news reports, data from official public health channels, and information supplied by doctors for warning signs the virus is taking hold in countries outside of China.

The program is looking for social media posts that mention specific symptoms, like respiratory problems and fever, from a geographic area where doctors have reported potential cases. Natural language processing is used to parse the text posted on social media, for example, to distinguish between someone discussing the news and someone complaining about how they feel. A company called BlueDot used a similar approachminus the social media sourcesto spot the coronavirus in late December, before Chinese authorities acknowledged the emergency.

We are moving to surveillance efforts in the US, Brownstein says. It is critical to determine where the virus may surface if the authorities are to allocate resources and block its spread effectively. Were trying to understand whats happening in the population at large, he says.

The rate of new infections has slowed slightly in recent days, from 3,900 new cases on Wednesday to 3,700 cases on Thursday to 3,200 cases on Friday, according to the World Health Organization. Yet it isnt clear if the spread is really slowing or if new infections are simply becoming more difficult to track.

So far, other countries have reported far fewer cases of coronavirus. But there is still widespread concern about the virus spreading. The US has imposed a travel ban on China even though experts question the effectiveness and ethics of such a move. Researchers at Johns Hopkins University have created a visualization of the viruss progress around the world based on official numbers and confirmed cases.

Health experts did not have access to such quantities of social, web, and mobile data when seeking to track previous outbreaks such as severe acute respiratory syndrome (SARS). But finding signs of the new virus in a vast soup of speculation, rumor, and posts about ordinary cold and flu symptoms is a formidable challenge. The models have to be retrained to think about the terms people will use and the slightly different symptom set, Brownstein says.

Even so, the approach has proven capable of spotting a coronavirus needle in a haystack of big data. Brownstein says colleagues tracking Chinese social media and news sources were alerted to a cluster of reports about a flu-like outbreak on December 30. This was shared with the WHO, but it took time to confirm the seriousness of the situation.

Beyond identifying new cases, Brownstein says the technique could help experts learn how the virus behaves. It may be possible to determine the age, gender, and location of those most at risk more quickly than using official medical sources.

Alessandro Vespignani, a professor at Northeastern University who specializes in modeling contagion in large populations, says it will be particularly challenging to identify new instances of the coronavirus from social media posts, even using the most advanced AI tools, because its characteristics still arent entirely clear. Its something new. We dont have historical data, Vespignani says. There are very few cases in the US, and most of the activity is driven by the media, by peoples curiosity.

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How AI Is Tracking the Coronavirus Outbreak - WIRED

The editors at Solutions Review curated this list of the best AI and machine learning TED talks for practitioners in the field.

TED Talks are influential videos from expert speakers in a variety of verticals. TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics from business to technology to global issues in more than 110 languages. TED is building a clearinghouse of free knowledge from the worlds top thinkers, and their library of videos is expansive and rapidly growing.

Solutions Review has curated this list of AI and machine learning TED talks to watch if you are a practitioner in the field. Talks were selected based on relevance, ability to add business value, and individual speaker expertise. Weve also curated TED talk lists for topics like data visualization and big data.

Erik Brynjolfsson is the director of the MIT Center for Digital Business and a research associate at the National Bureau of Economic Research. He asks how IT affects organizations, markets and the economy. His books include Wired for Innovation and Race Against the Machine. Brynjolfsson was among the first researchers to measure the productivity contributions of information and community technology (ICT) and the complementary role of organizational capital and other intangibles.

In this talk, Brynjolfsson argues that machine learning and intelligence are not the end of growth its simply the growing pains of a radically reorganized economy. A riveting case for why big innovations are ahead of us if we think of computers as our teammates. Be sure to watch the opposing viewpoint from Robert Gordon.

Jeremy Howard is the CEO ofEnlitic, an advanced machine learning company in San Francisco. Previously, he was the president and chief scientist atKaggle, a community and competition platform of over 200,000 data scientists. Howard is a faculty member atSingularity University, where he teaches data science. He is also a Young Global Leader with the World Economic Forum, and spoke at the World Economic Forum Annual Meeting 2014 on Jobs for the Machines.

Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis.

Nick Bostrom is a professor at the Oxford University, where he heads theFuture of Humanity Institute, a research group of mathematicians, philosophers and scientists tasked with investigating the big picture for the human condition and its future. Bostrom was honored as one ofForeign Policys 2015Global Thinkers. His bookSuperintelligenceadvances the ominous idea that the first ultraintelligent machine is the last invention that man need ever make.

In this talk, Nick Bostrom calls machine intelligence the last invention that humanity will ever need to make. Bostrom asks us to think hard about the world were building right now, driven by thinking machines. Will our smart machines help to preserve humanity and our values or will they have values of their own?

Lis work with neural networks and computer vision (with Stanfords Vision Lab) marks a significant step forward for AI research, and could lead to applications ranging from more intuitive image searches to robots able to make autonomous decisions in unfamiliar situations. Fei-Fei was honored as one ofForeign Policys 2015Global Thinkers.

This talk digs into how computers are getting smart enough to identify simple elements. Computer vision expert Fei-Fei Li describes the state of the art including the database of 15 million photos her team built to teach a computer to understand pictures and the key insights yet to come.

Anthony Goldbloom is the co-founder and CEO ofKaggle. Kaggle hosts machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists. In 2011 and 2012,Forbesnamed Anthony one of the 30 under 30 in technology; in 2013 theMIT Tech Reviewnamed him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award.

This talk by Anthony Goldbloom describes some of the current use cases for machine learning, far beyond simple tasks like assessing credit risk and sorting mail.

Tufekci is a contributing opinion writer at theNew York Times, an associate professor at the School of Information and Library Science at University of North Carolina, Chapel Hill, and a faculty associate at Harvards Berkman Klein Center for Internet and Society. Her book,Twitter and Tear Gas was published in 2017 by Yale University Press.

Machine intelligence is here, and were already using it to make subjective decisions. But the complex way AI grows and improves makes it hard to understand and even harder to control. In this cautionary talk, techno-sociologist Zeynep Tufekci explains how intelligent machines can fail in ways that dont fit human error patterns and in ways we wont expect or be prepared for.

In his bookThe Business Romantic, Tim Leberecht invites us to rediscover romance, beauty and serendipity by designing products, experiences, and organizations that make us fall back in love with our work and our life. The book inspired the creation of the Business Romantic Society, a global collective of artists, developers, designers and researchers who share the mission of bringing beauty to business.

In this talk, Tim Leberecht makes the case for a new radical humanism in a time of artificial intelligence and machine learning. For the self-described business romantic, this means designing organizations and workplaces that celebrate authenticity instead of efficiency and questions instead of answers. Leberecht proposes four principles for building beautiful organizations.

Grady Booch is Chief Scientist for Software Engineering as well as Chief Scientist for Watson/M at IBM Research, where he leads IBMs research and development for embodied cognition. Having originated the term and the practice of object-oriented design, he is best known for his work in advancing the fields of software engineering and software architecture.

Grady Booch allays our worst (sci-fi induced) fears about superintelligent computers by explaining how well teach, not program, them to share our human values. Rather than worry about an unlikely existential threat, he urges us to consider how artificial intelligence will enhance human life.

Tom Gruberis a product designer, entrepreneur, and AI thought leader who uses technology to augment human intelligence. He was co-founder, CTO, and head of design for the team that created theSiri virtual assistant. At Apple for over 8 years, Tom led the Advanced Development Group that designed and prototyped new capabilities for products that bring intelligence to the interface.

This talk introduces the idea of Humanistic AI. He shares his vision for a future where AI helps us achieve superhuman performance in perception, creativity and cognitive function from turbocharging our design skills to helping us remember everything weve ever read. The idea of an AI-powered personal memory also extends to relationships, with the machine helping us reflect on our interactions with people over time.

Stuart Russell is a professor (and formerly chair) of Electrical Engineering and Computer Sciences at University of California at Berkeley. His bookArtificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in more than 1,300 universities in 118 countries. He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty.

His talk centers around the question of whether we can harness the power of superintelligent AI while also preventing the catastrophe of robotic takeover. As we move closer toward creating all-knowing machines, AI pioneer Stuart Russell is working on something a bit different: robots with uncertainty. Hear his vision for human-compatible AI that can solve problems using common sense, altruism and other human values.

Dr. Pratik Shahs research creates novel intersections between engineering, medical imaging, machine learning, and medicine to improve health and diagnose and cure diseases. Research topics include: medical imaging technologies using unorthodox artificial intelligence for early disease diagnoses; novel ethical, secure and explainable artificial intelligence based digital medicines and treatments; and point-of-care medical technologies for real world data and evidence generation to improve public health.

TED Fellow Pratik Shah is working on a clever system to do just that. Using an unorthodox AI approach, Shah has developed a technology that requires as few as 50 images to develop a working algorithm and can even use photos taken on doctors cell phones to provide a diagnosis. Learn more about how this new way to analyze medical information could lead to earlier detection of life-threatening illnesses and bring AI-assisted diagnosis to more health care settings worldwide.

Margaret Mitchells research involves vision-language and grounded language generation, focusing on how to evolve artificial intelligence towards positive goals. Her work combines computer vision, natural language processing, social media as well as many statistical methods and insights from cognitive science. Before Google, Mitchell was a founding member of Microsoft Researchs Cognition group, focused on advancing artificial intelligence, and a researcher in Microsoft Researchs Natural Language Processing group.

Margaret Mitchell helps develop computers that can communicate about what they see and understand. She tells a cautionary tale about the gaps, blind spots and biases we subconsciously encode into AI and asks us to consider what the technology we create today will mean for tomorrow.

Kriti Sharma is the Founder of AI for Good, an organization focused on building scalable technology solutions for social good. Sharma was recently named in theForbes 30 Under 30 list for advancements in AI. She was appointed a United Nations Young Leader in 2018 and is an advisor to both the United Nations Technology Innovation Labs and to the UK Governments Centre for Data Ethics and Innovation.

AI algorithms make important decisions about you all the time like how much you should pay for car insurance or whether or not you get that job interview. But what happens when these machines are built with human bias coded into their systems? Technologist Kriti Sharma explores how the lack of diversity in tech is creeping into our AI, offering three ways we can start making more ethical algorithms.

Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MITs Institute for the Digital Economy. He received his PhD from the MIT Sloan School of Management.

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, were handling AI in a way that blocks that path and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AIs amazing capabilities while enhancing our skills at the same time.

Leila Pirhaji is the founder ofReviveMed, an AI platform that can quickly and inexpensively characterize large numbers of metabolites from the blood, urine and tissues of patients. This allows for the detection of molecular mechanisms that lead to disease and the discovery of drugs that target these disease mechanisms.

Biotech entrepreneur and TED Fellow Leila Pirhaji shares her plan to build an AI-based network to characterize metabolite patterns, better understand how disease develops and discover more effective treatments.

Janelle Shane is the owner of AIweirdness.com. Her book, You Look Like a Thing and I Love Youuses cartoons and humorous pop-culture experiments to look inside the minds of the algorithms that run our world, making artificial intelligence and machine learning both accessible and entertaining.

The danger of artificial intelligence isnt that its going to rebel against us, but that its going to do exactly what we ask it to do, says AI researcher Janelle Shane. Sharing the weird, sometimes alarming antics of AI algorithms as they try to solve human problems like creating new ice cream flavors or recognizing cars on the road Shane shows why AI doesnt yet measure up to real brains.

Sylvain Duranton is the global leader of BCG GAMMA, a unit dedicated to applying data science and advanced analytics to business. He manages a team of more than 800 data scientists and has implemented more than 50 custom AI and analytics solutions for companies across the globe.

In this talk, business technologist Sylvain Duranton advocates for a Human plus AI approach using AI systems alongside humans, not instead of them and shares the specific formula companies can adopt to successfully employ AI while keeping humans in the loop.

For more AI and machine learning TED talks, browse TEDs complete topic collection.

Timothy is Solutions Review's Senior Editor. He is a recognized thought leader and influencer in enterprise BI and data analytics. Timothy has been named a top global business journalist by Richtopia. Scoop? First initial, last name at solutionsreview dot com.

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The 17 Best AI and Machine Learning TED Talks for Practitioners - Solutions Review