Its easier to program bias out of a machine than out of a mind.

Thats an emerging conclusion of research-based findings including my own that could lead to AI-enabled decision-making systems being less subject to bias and better able to promote equality. This is a critical possibility, given our growing reliance on AI-based systems to render evaluations and decisions in high-stakes human contexts, in everything from court decisions, to hiring, to access to credit, and more.

Its been well-established that AI-driven systems are subject to the biases of their human creators we unwittingly bake biases into systems by training them on biased data or with rules created by experts with implicit biases.

Consider the Allegheny Family Screening Tool (AFST), an AI-based system predicting the likelihood a child is in an abusive situation using data from the same-named Pennsylvania countys Department of Human Services including records from public agencies related to child welfare, drug and alcohol services, housing, and others. Caseworkers use reports of potential abuse from the community, along with whatever publicly-available data they can find for the family involved, to run the model, which predicts a risk score from 1 to 20; a sufficiently high score triggers an investigation. Predictive variables include factors such as receiving mental health treatment, accessing cash welfare assistance, and others.

Sounds logical enough, but theres a problem a big one. By multiple accounts, the AFST has built-in human biases. One of the largest is that the system heavily weights past calls about families, such as from healthcare-providers, to the community hotline and evidence suggests such calls are over three times more likely to involve Black and biracial families than white ones. Though multiple such calls are ultimately screened out, the AFST relies on them in assigning a risk score, resulting in potentially racially-biased investigations if callers to the hotline are more likely to report Black families than non-Black families, all else being equal. This can result in an ongoing, self-fulfilling, and self-perpetuating prophecy where the training data of an AI system can reinforce its misguided predictions, influencing future decisions and institutionalizing the bias.

It doesnt have to be this way. More strategic use of AI systems through what I call blind taste tests can give us a fresh chance to identify and remove decision biases from the underlying algorithms even if we cant remove them completely from our own habits of mind. Breaking the cycle of bias in this way has the potential to promote greater equality across contexts from business to science to the arts on dimensions including gender, race, socioeconomic status, and others.

Blind taste tests have been around for decades.

Remember the famous Pepsi Challenge from the mid-1970s? When people tried Coca-Cola and Pepsi blind no labels on the cans the majority preferred Pepsi over its better-selling rival. In real life, though, simply knowing it was Coke created a bias in favor of the product; removing the identifying information the Coke label removed the bias so people could rely on taste alone.

In a similar blind test from the same time period, wine experts preferred California wines over their French counterparts, in what became known as the Judgment of Paris. Again, when the label is visible, the results are very different, as experts ascribe more sophistication and subtlety to the French wines simply because theyre French indicating the presence of bias yet again.

So its easy to see how these blind taste tests can diminish bias in humans by removing key identifying information from the evaluation process. But a similar approach can work with machines.

That is, we can simply deny the algorithm the information suspected of biasing the outcome, just as they did in the Pepsi Challenge, to ensure that it makes predictions blind to that variable. In the AFST example, the blind taste test could work like this: train the model on all data, including referral calls from the community. Then re-train the model on all the data except that one. If the models predictions are equally good without referral-call information, it means the model makes predictions that are blind to that factor. But if the predictions are different when those calls are included, it indicates that either the calls represent a valid explanatory variable in the model, or there may be potential bias in the data (as has been argued for the AFST) that should be examined further before relying on the algorithm.

This process breaks the self-perpetuating, self-fulfilling prophecy that existed in the human system without AI, and keeps it out of the AI system.

My research with Kellogg collaborators Yang Yang and Youyou Wu demonstrated a similar anti-bias effect in a different domain: the replicability of scientific papers.

What separates science from superstition is that a scientific fact that is found in the lab or a clinical trial replicates out in the real world again and again. When it comes to evaluating the replicability or reproducibility of published scientific results, we humans struggle.

Some replication failure is expected or even desirable because science involves experimentation of unknowns. However, an estimated 68% of studies published in medicine, biology, and social science papers do not replicate. Replication failures continue to be unknowingly cited in the literature, driving up R&D costs by an estimated $28 billion annually and slowing discoveries of vaccines and therapies for Covid-19 and other conditions.

The problem is related to bias: when scientists and researchers review a manuscript for publication, they focus on a papers statistical and other quantitative results in judging replicability. That is, they use the numbers in a scientific paper much more than the papers narrative, which describes the numbers, in making this assessment. Human reviewers are also influenced by institutional labels (e.g., Cambridge University), scientific discipline labels (physicists are smart), journal names, and other status biases.

To address this issue, we trained a machine-learning model to estimate a papers replicability using only the papers reported statistics (typically used by human reviewers), narrative text (not typically used), or a combination of these. We studied 2 million abstracts from scientific papers and over 400 manually-replicated studies from 80 journals.

The AI model using only the narrative predicted replicability better than the statistics. It also predicted replicability better than the base rate of individual reviewers, and as well as prediction markets, where collective intelligence of hundreds of researchers is used to assess a papers replicability, a very costly approach. Importantly, we then used the blind taste test approach and showed that our models predictions werent biased by factors including topic, scientific discipline, journal prestige, or persuasion words like unexpected or remarkable. The AI model provided predictions of replicability at scale and without known human biases.

In a subsequent extension of this work (in progress), we again used an AI system to reexamine the scientific papers in the study that had inadvertently published numbers and statistics that contained mistakes that the reviewers hadnt caught during the review process, likely due to our general tendency to believe figures we are shown. Again, a system blind to variables that can promote bias when over-weighted in the review process quantitative evidence, in this case was able to render a more objective evaluation than humans alone could, catching mistakes missed due to bias.

Together, the findings provide strong evidence for the value of creating blind taste tests for AI systems, to reduce or remove bias and promote fairer decisions and outcomes across contexts.

The blind-taste-test concept can be applied effectively to reduce bias in multiple domains well beyond the world of science.

Consider earnings calls led by business C-suite teams to explain recent and projected financial performance to analysts, shareholders, and others. Audience members use the content of these calls to predict future company performance, which can have large, swift impact on share prices and other key outcomes.

But again, human listeners are biased to use the numbers presented just as in judging scientific replicability and to pay excessive attention to who is sharing the information (a well-known CEO like Jeff Bezos or Elon Musk versus someone else). Moreover, companies have an incentive to spin the information to create more favorable impressions.

An AI system can look beyond potential bias-inducing information to factors including the text of the call (words rather than numbers) and others such as the emotional tone detected, to render more objective inputs for decision-making. We are currently examining earnings-call data with this hypothesis in mind, along with studying specific issues such as whether the alignment between numbers presented and the verbal description of those numbers has an equal effect on analysts evaluations if the speaker is male or female. Will human evaluators give men more of a pass in the case of misalignment? If we find evidence of bias, it will indicate that denying gender information to an AI system can yield more equality-promoting judgments and decisions related to earnings calls.

We are also applying the ideas here to the patents domain, where patent applications involve a large investment and rejection rates are as high as 50%. Here, current models used to predict a patent applications success or a patents expected value dont perform much better than chance, and tend to use factors like whether an individual or team filed the application, again suggesting potential bias. We are studying the value of using AI systems to examine patent text, to yield more effective, fairer judgments.

There are many more potential applications of the blind-taste-test approach. What if interviews for jobs or assessments for promotions or tenure took place with some kind of blinding mechanism in place, preventing the biased use of gender, race, or other variables in decisions? What about decisions for which startup founders receive funding, where gender bias has been evident? What if judgments about who received experimental medical treatments were stripped of potential bias-inducing variables?

To be clear, Im not suggesting that we use machines as our sole decision-making mechanisms. After all, humans can also intentionally program decision-making AI systems to manipulate information. Still, our involvement is critical to form hypotheses about where bias may enter in the first place, and to create the right blind taste tests to avoid it. Thus, an integration of human and AI systems is the optimal approach.

In sum, its fair to conclude that the human condition inherently includes the presence of bias. But increasing evidence suggests we can minimize or overcome that by programming bias out of the machine-based systems we use to make critical decisions, creating a more equal playing field for all.

Originally posted here:

A Simple Tactic That Could Help Reduce Bias in AI - Harvard Business Review

The story of Ted Chiangs life includes stints as a technical writer in the Seattle area and worldwide acclaim as a science-fiction writer. (Alan Berner Photo via Knopf Doubleday Publicity)

What rights does a robot have? If our machines become intelligent in the science-fiction way, thats likely to become a complicated question and the humans who nurture those robots just might take their side.

Ted Chiang, a science-fiction author of growing renown with long-lasting connections to Seattles tech community, doesnt back away from such questions. They spark the thought experiments that generate award-winning novellas like The Lifecycle of Software Objects, and inspire Hollywood movies like Arrival.

Chiangs soulful short stories have earned him kudos from the likes of The New Yorker, which has called him one of the most influential science-fiction writers of his generation. During this years pandemic-plagued summer, he joined the Museum of Pop Cultures Science Fiction and Fantasy Hall of Fame. And this week, hes receiving an award from the Arthur C. Clarke Foundation for employing imagination in service to society.

Can science fiction have an impact in the real world, even at times when the world seems as if its in the midst of a slow-moving disaster movie?

Absolutely, Chiang says.

Art is one way to make sense of a world which, on its own, does not make sense, he says in the latest episode of the Fiction Science podcast, which focuses on the intersection between science and fiction. Art can impose a kind of order onto things. It doesnt offer a cure-all, because I dont think theres going to be any easy cure-all, but I think art helps us get by in these stressful times.

COVID-19 provides one illustration. Chiang would argue that our response to the coronavirus pandemic has been problematic in part because it doesnt match what weve seen in sci-fi movies.

The greatest conflict that we see generated is from people who dont believe in it vs. everyone else, he said. That might be the product of the fact that it is not as severe. If it looked like various movie pandemics, itd probably be hard for anyone to deny that it was happening.

This pandemic may well spark a new kind of sci-fi theme.

Its worth thinking about, that traditional depictions of pandemics dont spend much time on people coming together and trying to support each other, Chiang said. That is not typically a theme in stories about disaster or enormous crisis. I guess the narrative is usually, Its the end of civilization. And people have not turned on each other in that way.

Artificial intelligence is another field where science fiction often gives people the wrong idea. When we talk about AI in science fiction, were talking about something very different than what we mean when we say AI in the context of current technology, he said.

Chiang isnt speaking here merely as an author of short stories, but as someone who joined the Seattle tech community three decades ago to work at Microsoft as a technical writer. During his first days in Seattle, his participation in 1989s Clarion West Science Fiction and Fantasy Writers Workshop helped launch his second career as a fiction writer.

In our interview, Chiang didnt want to say much about the technical-writing side of his career, but his expertise showed through in our discussion about real vs. sci-fi AI. When people talk about AI in the real world theyre talking about a certain type of software that is usually like a superpowered version of applied statistics, he said.

Thats a far cry from the software-enhanced supervillains of movies like Terminator or The Matrix, or the somewhat more sympathetic characters in shows like Westworld and Humans.

In Chiangs view, most depictions of sci-fi AI fall short even by science-fiction standards. A lot of stories imagine something which is a product like a robot that comes in a box, and you flip it on, and suddenly you have a butler a perfectly competent and loyal and obedient butler, he noted. That, I think jumps over all these steps, because butlers dont just happen.

In The Lifecycle of Software Objects, Chiang imagines a world in which it takes just as long to raise a robot as it does to raise a child. That thought experiment sparks all kinds of interesting all-too-human questions: What if the people who raise such robots want them to be something more than butlers? Would they stand by and let their sci-fi robot progeny be treated like slaves, even like sex slaves?

Maybe they want that robot, or conscious software, to have some kind of autonomy, Chiang said. To have a good life.

Chiangs latest collection of short stories, Exhalation, extends those kinds of thought experiments to science-fiction standbys ranging from free will to the search for extraterrestrial intelligence.

Both those subjects come into play in whats certainly Chiangs best-known novella, Story of Your Life, which was first published in 1998 and adapted to produce the screenplay for Arrival in 2016. Like so many of Chiangs other stories, Story of Your Life takes an oft-used science-fiction trope in this case, first contact with intelligent aliens and adds an unexpected but insightful and heart-rending twist.

Chiang said that the success of the novella and the movie hasnt led to particularly dramatic changes in the story of his own life, but that it has broadened the audience for the kinds of stories he tells.

My work has been read by people who would not describe themselves as science-fiction readers, by people who dont usually read a lot of science fiction, and thats been amazing. Thats been really gratifying, he said. Its not something that I ever really expected.

Whats more, Chiangs work has been popping up in places where you wouldnt expect to see science fiction such as The New York Times, where he weighs in on the implications of human gene editing; or Buzzfeed News, where he reflects on the downside of Silicon Valleys world view; or the journal Nature, where you can find Chiangs thought experiments on free will and transhumanism; or Nautilus, where Chiang offers an unorthodox perspective on SETI.

During our podcast chat, Chiang indulged in yet another thought experiment: Could AI replace science-fiction writers?

Chiangs answer? It depends.

If we could get software-generated novels that were coherent, but not necessarily particularly good, I think there would be a market for them, he said.

But Chiang doesnt think that would doom human authors.

For an AI to generate a novel that you think of as really good, that you feel like, Oh, wow, this novel was both gripping and caused me to think about my life in a new way that, I think, is going to be very, very hard, he said.

Ted Chiang only makes it look easy.

Chiang and other Arthur C. Clarke Foundation awardees will take part in the 2020 Clarke Conversation on Imagination at 9 a.m. PT Nov. 12. Register via the foundations website and Eventbrite to get in on the interactive video event.

This is a version of an article first published on Cosmic Log. Check out the Cosmic Log posting for Ted Chiangs reading recommendations, which are this months selections for the Cosmic Log Used Book Club.

My co-host for the Fiction Science podcast is Dominica Phetteplace, an award-winning writerwho is a graduate of theClarion West Writers Workshopand currently lives in Berkeley, Calif. Shes among the science-fiction authors featured inThe Best Science Fiction of the Year. To learn more about Phetteplace, check out her website,DominicaPhetteplace.com.

Here is the original post:

Science-fiction master Ted Chiang explores the rights and wrongs of AI - GeekWire

Which candidate should we hire? Who should be promoted? How should we choose which people get which shifts? In the hope of making better and fairer decisions about personnel matters such as these, companies have increasingly adopted AI tools only to discover that they may have biases as well. How can we decide whether to keep human managers or go with AI? This article offers four considerations.

The initial promise of artificial intelligence as a broad-based tool for solving business problems has given way to something much more limited but still quite useful: algorithms from data science that make predictions better than we have been able to do so far.

In contrast to standard statistical models that focus on one or two factors already known to be associated with an outcome like job performance, machine-learning algorithms are agnostic about which variables have worked before or why they work. The more the merrier: It throws them all together and produces one model to predict some outcome like who will be a good hire, giving each applicant a single, easy-to-interpret score as to how likely it is that they will perform well in a job.

No doubt because the promise of these algorithms was so great, the recognition of their limitations has also gotten a lot of attention, especially given the fact that if the initial data used to build the model is biased, then the algorithm generated from that data will perpetuate that bias. The best-known examples have been in organizations that discriminated against women in the past where job performance data is also biased, and that means algorithms based on that data will also be biased.

So how should employers proceed as they contemplate adopting AI to make personnel decisions? Here are four considerations:

1. The algorithm may be less biased than the existing practices that generate the data in the first place. Lets not romanticize how poor human judgment is and how disorganized most of our people management practices are now. When we delegate hiring to individual supervisors, for example, it is quite likely that they may each have lots of biases in favor of and against candidates based on attributes that have nothing to do with good performance: Supervisor A may favor candidates who graduated from a particular college because she went there, while Supervisor B may do the reverse because he had a bad experience with some of its graduates. At least algorithms treat everyone with the same attributes equally, albeit not necessarily fairly.

2. We may not have good measures of all of the outcomes we would like to predict, and we may not know how to weight the various factors in making final decisions. For example, what makes for a good employee? They have to accomplish their tasks well, they also should get along with colleagues well, fit in with the culture, stay with us and not quit, and so forth. Focusing on just one aspect where we have measures will lead to a hiring algorithm that selects on that one aspect, often when it does not relate closely to other aspects, such as a salesperson who is great with customers but miserable with co-workers.

Here again, it isnt clear that what we are doing now is any better: An individual supervisor making a promotion decision may be able in theory to consider all those criteria, but each assessment is loaded with bias, and the way they are weighted is arbitrary. We know from rigorous research that the more hiring managers use their own judgment in these matters, the worse their decisions are.

3. The data that AI uses may raise moral issues. Algorithms that predict turnover, for example, now often rely on data from social media sites, such as Facebook postings. We may decide that it is an invasion of privacy to gather such data about our employees, but not using it comes at the price of models that will predict less well.

It may also be the case that an algorithm does a good job overall in predicting something for the average employee but does a poor job for some subset of employees. It might not be surprising, for example, to find that the hiring models that pick new salespeople do not work well at picking engineers. Simply having separate models for each would seem to be the solution. But what if the different groups are men and women or whites and African Americans, as appears to be the case? In those cases, legal constraints prevent us from using different practices and different hiring models for different demographic groups.

4. It is often hard, if not impossible, to explain and justify the criteria behind algorithmic decisions. In most workplaces now, we at least have some accepted criteria for making employment decisions: He got the opportunity because he has been here longer; she was off this weekend because she had that shift last weekend; this is the way we have treated people before. If I dont get the promotion or the shift I want, I can complain to the person who made the decision. He or she has a chance to explain the criterion and may even help me out next time around if the decision did not seem perfectly fair.

When we use algorithms to drive those decisions, we lose the ability to explain to employees how those decisions were made. The algorithm simply pulls together all the available information to construct extremely complicated models that predict past outcomes. It would be highly unlikely if those outcomes corresponded to any principle that we could observe or explain other than to say, The overall model says this will work best. The supervisor cant help explain or address fairness concerns.

Especially where such models do not perform much better than what we are already doing, it is worth asking whether the irritation they will cause employees is worth the benefit. The advantage, say, of just letting the most senior employee get first choice in picking his or her schedule is that this criterion is easily understood, it corresponds with at least some accepted notions of fairness, it is simple to apply, and it may have some longer-term benefits, such as increasing the rewards for sticking around. There may be some point where algorithms will be able to factor in issues like this, but we are nowhere close to that now.

Algorithmic models are arguably no worse than what we are doing now. But their fairness problems are easier to spot because they happen at scale. The way to solve them is to get more and better measures data that is not biased. Doing that would help even if we were not using machine-learning algorithms to make personnel decisions.

More here:

4 Things to Consider Before You Start Using AI in Personnel Decisions - Harvard Business Review

TLDR: With the KarenApp Scheduling Software, you can let customers book appointments, receive reminders and even make payments, all automatically.

If youre a busy entrepreneur or a freelance professional, booking appointments can turn into a major headache. Thats because it isnt just about scheduling an appointment. Its about making sure youve included everyone that needs to be included. Its about everyone knowing where a virtual or in-person meeting is happening. And if your hours are billable, its definitely about making sure youre getting paid for your time.

The KarenApp Scheduling Software isnt just a scheduling app. Its an AI-driven personal booking assistant that can help you or your entire organization all but fully automate your calendars without the needless flurry of back-and-forth emails, miscommunications and other logistical nightmares.

KarenApp connects to your Google calendar and gets to work, letting you talk to your calendar in natural language. If you want to schedule a meeting next Thursday, the Karen AI understands when next Thursday is and will get it done. And the more you use Karen, the better shell come to understand your preferences.

Once you enter information about your particular business, KarenApp generates an engaging landing page so potential customers can book appointments easily. With pricing included on your page, anyone who wants to see you will not only know when youre available so they can book a time that works for everyone, but also what your services will cost.

Once an appointment is set, KarenApp will automatically send reminders about your meeting to all parties; as well as allow you to access information about that client or alert and include other team members that should be involved.

If youre meeting virtually, KarenApp lets you automatically add a Zoom link to the appointment. And when you want to get paid for an appointment upfront, customers can connect their Stripe account and automatically pay for their meeting through KarenApps secured interface. Users can also expect an option for PayPal payments coming soon.

With the $49.99 KarenApp Nest Plan, three team members can schedule up to 100 meetings per month on this lifetime subscription thats regularly a $490 value. For larger teams, you can also upgrade with similar savings to a 6-member, 500-meeting Hive Plan ($99.99) or a full 12-member, 1,000-meeting Woods Plan ($149.99).

Prices are subject to change.

Read next: This VPN and password manager from Nord protects your web connection and passwords at over 70% off

Continued here:

This AI-driven appointment booker takes annoying scheduling out of your hands - The Next Web

Many of us may not even understand exactly where or what the Cloud is.

Yet, much of the data and programs that control our lives live on this Cloud of distant computer servers with the directions to run our devices coming over the Internet.

As the prevalence of artificial intelligence (AI)-driven devices grows, researchers would like to bring some of that decision-making back to our own devices. WSU researchers have developed a novel framework to more efficiently use AI algorithms on mobile platforms and other portable devices. They presented their most recent work at the 2020 Design Automation Conference and the 2020 International Conference on Computer Aided Design.

The goal is to push intelligence to mobile platforms that are resource-constrained in terms of power, computation, and memory, said Jana Doppa, George and Joan Berry Associate Professor in the School of Electrical Engineering and Computer Science. This has a huge number of applications ranging from mobile health, augmented and virtual reality, self-driving cars, digital agriculture, and image and video processing mobile applications.

Voice-recognition software, mobile health, robotics, and Internet-of-Things devices all use artificial intelligence to keep society moving at an ever-faster and automated pace. Self-driving cars powered by AI algorithms remain somewhere on the not-too-distant horizon.

The decisions for these increasingly sophisticated devices are all made in the Cloud, but as demands increase, the Cloud can become increasingly problematic, Doppa said. For instance, it isnt fast enough. Having a device in a self-driving car decide to turn right while looking both ways requires that information go from the car to the Cloud and then back to the car.

The time required to make decisions might not meet real-time requirements, said Partha Pande, Boeing Centennial Chair professor in School of EECS, who collaborated in this research.

Many rural or under-developed areas also dont have easy access to the infrastructure needed for the requirements of AI related communications and transferring information back and forth through the Cloud can also raise privacy concerns.

At the same time, however, requiring sophisticated computer algorithms to run on portable devices is also problematic. Computational resources havent been good enough, a phones computing memory is small, and a lot of decision-making will quickly drain the battery power.

We need to run the algorithms in a resource-constrained environment, Pande said.

Doppas group came up with a framework that is able to run complex neural network-based algorithms locally using less power and computation.

The researchers took an approach that prioritizes problem solving. As in human decision-making in which problems vary in their complexity and require more or less brain power, the researchers developed a framework in which their algorithms spend a lot of energy on only the complex parts of problems while using less resources for the easy ones.

By doing this, we are improving performance and saving a lot of energy, Doppa said.

So, for instance, in a digital agriculture application, their more efficient software and hardware could be embedded on a UAV, which could efficiently make decisions about crop spraying with less computational and energy requirements.

The researchers have applied their algorithms to virtual/augmented reality as well as image editing applications. The researchers are the first to adapt state-of-the-art AI approaches for structured outputs to a mobile platform. These include Graph Convolution Networks (GCNs), which are used to produce three-dimensional object shapes from images in augmented and virtual reality, and Generative Adversarial Networks (GANs) technology, which is used to generate synthetic images. In the case of the GAN technology, the solution the researchers developed was able to achieve a more than 50% savings in energy for a loss of about 10% in accuracy.

Since mobile platforms are constrained by resources, there is a great need for low-overhead solutions for these emerging GCNs and GANs to perform energy-constrained inference, said Nitthilan Kanappan Jayakodi, a graduate student in the School of Electrical Engineering and Computer Science who was lead author on the research and was selected as a Richard Newton Young Fellow from the ACM Special Interest Group on Design Automation for his outstanding research contributions. To the best of our knowledge, this is the first work on studying methods to deploy emerging GCNs and GANs to predict complex structured outputs on mobile platforms.

The work was funded by the National Science Foundation and the U.S. Army Research Office.

Go here to read the rest:

Artificial intelligence on the edge | WSU Insider | Washington State University - WSU News

Global Artificial Intelligence AI in the Freight TransportationMarket 2020-2026 Key Challenges. Industry Risks and Worldwide Opportunities during Covid-19.

Market Research Vision published latest Research Report on Global Artificial Intelligence AI in the Freight TransportationMarket 2020. Research study explores economical impact of pandemic on Artificial Intelligence AI in the Freight Transportationindustry Segment as follows:

By Important Manufacturers KONUX, Cargofy, FERO

Get Latest Sample Report of Global Artificial Intelligence AI in the Freight TransportationMarket 2020-2026: https://www.marketresearchvision.com/request-sample/548905

The report has segmented market 20202025 into different components on the basis of products, Application, geography and end users wherever needed. With thorough analysis and detailed study of past, present and future market 20162021 conditions, the report is able to delivery factual and reliable information to the users.

By Region / Countries

Click here to Get customization & check discount for the report @ https://www.marketresearchvision.com/check-discount/548905

Our Research Experts have made this insightful report on Artificial Intelligence AI in the Freight Transportationmarket, which is available for user on the site of Market Research Vision. The report carries various factors and elements of the industry in picture, all around the globe, be it its potential or probable threat to its growth or simply the way and method of its functioning during covid-19.

About Market Research Vision

We at Market Research Vision provides a study of products, services, technologies, applications, end users, market dynamics, and market players for global, regional, and country level market segments. Our team widely focuses on market dynamics and identifies new opportunities to rely on. We serve several fortune 500 clients along with various small and large companies. We serve in several domains such as aerospace and defense, automotive and transportation, banking & finance, construction & manufacturing, machinery & equipment, company profiles, consumer goods, food and beverages, ICT media, energy & power, materials and chemicals, medical devices, pharmaceuticals & healthcare, and semiconductor & electronics.

Fill FREE with your queries to get a call from our research expert @https://www.marketresearchvision.com/reports/548905/Artificial-Intelligence-AI-in-the-Freight-Transportation-Market

Contact Us

Mr. Elvis Fernandes

Phone:

+1 513 549 5911 (US)

+44 203 318 3262 (UK)

Email: [emailprotected]

Read more from the original source:

Artificial Intelligence AI in the Freight Transportation Market By Business Outlook, Segmentation and Top Key Players - PRnews Leader

This newly added research report on global AI in Social Media market represents an elaborate description of the market scenario, analyzing the industry developments across timelines to influencing accurate forecast predictions.

The report specifically determines dominant AI in Social Media market developments and events that are influenced by macro and micro economic factors. The report denotes crucial information delivery encompassing primary and secondary information that have been sourced across multiple platforms.

Access the PDF sample of AI in Social Media market report @ https://www.orbisresearch.com/contacts/request-sample/4051803?utm_source=Atish

This report focuses on the global top players, coveredGoogleFacebookMicrosoftAWSIBMAdobe SystemsBaiduSalesforceTwitterSnapClarabridgeConverseonSprinklrUnmetricIsentiumCluepNetbaseSpredfastSynthesioCrimson HexagonHootsuiteSprout SocialVidoraMeltwaterTalkwalker

A detailed competition analysis has also been included in the report to deliver insightful understanding on core vendors, leading players as well as their effective growth strategies based on which new and established players can deploy remunerative business decisions.

Make an enquiry of this report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/4051803?utm_source=Atish

Segment by Type, the product can be split intoMachine Learning and Deep LearningNatural Language Processing (NLP)

By Application, the market can be split intoRetail and eCommerceBanking, Financial Services, and Insurance (BFSI)Media and AdvertisingEducationPublic UtilitiesOthers

The report delivers crucial details on primary applications of the product and services that align with end-user requirements. The report sheds light on management and production details incorporating detailed assessment of trends that play crucial roles in decision enablement across businesses. The report also delivers details on vendor landscape and commercial environment.

Browse the complete AI in Social Media market report @ https://www.orbisresearch.com/reports/index/global-ai-in-social-media-market-report-history-and-forecast-2014-2025-breakdown-data-by-companies-key-regions-types-and-application?utm_source=Atish

A crucial reference data on competition spectrum has also been included in the report to identify their competition management tricks besides understanding their AI in Social Media market stance across geographical terrains and growth hubs. Each of the players marked in the report has been specifically assessed to derive logical deductions of their tactical decisions. As well as performance.

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: +1 (972)-362-8199 ; +91 895 659 5155

See original here:

AI in Social Media Market Key Growth Factors, development trends, key manufacturers and competitive forecast 2025 - The Think Curiouser

With the watchful use of established and advanced tools such as SWOT analysis and Porters Five Forces Analysis, this market report has been structured. Meticulous hard work of skilled forecasters, well-versed analysts and knowledgeable researchers gives outcome of such premium AI in Agriculture market research report. This market report aids to unearth the general market conditions, existing trends and tendencies in the industry. The market study conducted in this report analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, and distributors in the industry.

This AI in Agriculture market report studies the market and the industry thoroughly by considering several aspects. According to this market report, the global market is anticipated to observe a moderately higher growth rate during the forecast period. This makeover can be subjected to the moves of key players or brands which include developments, product launches, joint ventures, mergers and acquisitions that in turn change the view of the global face of the industry. With the actionable market insights included in this report, businesses can craft sustainable and cost-effective strategies. This AI in Agriculture market report provides all-inclusive study about production capacity, consumption, import and export for all the major regions across the world.

According to report published byData Bridge Market Research, TheAI in Agriculture marketsize is expected to reachUSD XX billionin the forecast period. This report provides in depth study of AI in Agriculture Market using SWOT analysis i.e. Strength, Weakness, Opportunities and Threat to the organization.

Get Exclusive FREE Sample Report with All Related Graphs & Charts @https://www.databridgemarketresearch.com/request-a-sample/?dbmr=global-ai-agriculture-market

Register Here @https://www.databridgemarketresearch.com/digital-conference/conference-on-content-moderation-solution

Key questions answered in the report:

What is the growth potential of this market?

Which product segment will grab a lions share?

Which regional market will emerge as a frontrunner in the coming years?

Which application segment will grow at a robust rate?

What are the growth opportunities that may emerge in this industry in the years to come?

What are the key challenges that the global market may face in the future?

Which are the leading companies in the global market?

Which are the key trends positively impacting the market growth?

Which are the growth strategies considered by the players to sustain hold in the global market?

Report includes Competitors Landscape:

Major trends and growth projections by region and country

Key winning strategies followed by the competitors

Who are the key competitors in this industry?

What shall be the potential of this industry over the forecast tenure?

What are the factors propelling the demand for this Industry?

What are the opportunities that shall aid in significant proliferation of the market growth?

What are the regional and country wise regulations that shall either hamper or boost the demand for this Industry?

How has the covid-19 impacted the growth of the market?

Has the supply chain disruption caused changes in the entire value chain?

Market segmentation

By Offering (Hardware, Software, Service, AI-As-A-Service), By Technology (Predictive Analytics, Machine Learning, Computer Vision), By Application (Livestock Monitoring, Precision Farming, Agriculture Robots, Livestock Monitoring, Drone Analytics), By Geography (North America, Europe, Asia-Pacific, Europe, South America, Middle East and Africa)

NOTE: Our report highlights the major issues and hazards that companies might come across due to the unprecedented outbreak of COVID-19.

The assessment provides a 360 view and insights, outlining the key outcomes of the industry, current scenario witnesses a slowdown and study aims to unique strategies followed by key players. These insights also help the business decision-makers to formulate better business plans and make informed decisions for improved profitability. In addition, the study helps venture or private players in understanding the companies more precisely to make better informed decisions.Some of the key players in the Global AI in Agriculture market are M, Microsoft Corporation, Descartes Labs, Deere & Company, Granular, aWhere, The Climate Corporation Agribotix LLC, Tule Technologies, Prospera, Mavrx Inc., Cropx, Harvest Croo, Farmbot, Trace Genomics, Spensa Technologies Inc., Resson, Vision Robotics and Autonomous Tractor Corporation among others

We can add or profile new company as per client need in the report. Final confirmation to be provided by research team depending upon the difficulty of survey

Why COVID-19 AI in Agriculture Research Insights is Interesting?

This report covers the current slowdown due to Coronavirus and growth prospects of COVID-19High AI in Agriculture for the period. The study is a professional and in-depth study with around n- no. of tables and figures which provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the domain to better understand how players are fighting and preparing against COVID-19.

Regional Analysis:

This segment of the report covers the analysis of AI in Agriculture consumption, import, export, market value, revenue, market share and growth rate, market status and SWOT analysis, price and gross margin analysis by regions. It includes data about several parameters related to the regional contribution. From the available data, we will identify which area has the largest share of the market. At the same time, we will compare this data to other regions, to understand the demand in other countries. Market analysis by regions: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, etc.), Middle East & Africa (Saudi Arabia, Egypt, Nigeria and South Africa)

Answers That The Report Acknowledges:

To know more about this research, you can click @https://www.databridgemarketresearch.com/reports/global-ai-agriculture-market

Research objectives:

To study and analyze the global AI in Agriculture market size by key regions/countries, product type and application, history data, and forecast period

To understand the structure of AI in Agriculture market by identifying its various sub segments.

Focuses on the key global AI in Agriculture players, to define, describe and analyze the value, market share, market competition landscape, SWOT analysis and development plans in next few years

To analyze the AI in Agriculture with respect to individual growth trends, future prospects, and their contribution to the total market

To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks)

To project the size of AI in Agriculture submarkets, with respect to key regions (along with their respective key countries)

To analyse competitive developments such as expansions, agreements, new product launches and acquisitions in the market

To strategically profile the key players and comprehensively analyze their growth strategies

Key Pointers Covered within the Global AI in Agriculture Market Industry Trends and Forecast

And More..Get Detailed Free TOC @https://www.databridgemarketresearch.com/toc/?dbmr=global-ai-agriculture-market

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market.

Contact:

Tel: +1-888-387-2818

Email: Corporatesales@databridgemarketresearch.com

More:

AI in Agriculture market Comprehensive Analysis On Size (Value & Volume), Application | The Climate Corporation Agribotix LLC, Tule Technologies,...

A chara, Paul Connolly is correct to challenge the assumption that if something produces better outcomes then it does not matter whether its human or machine (Letters, November 5th).

The current lack of significant ethical frameworks, underpinned by law, around the competing usages of artificial intelligence (AI) by so many global commercial firms has been noted in several reports.

Machine intelligence is certainly assisting us to accelerate our learning in ways that we have never experienced before. However, this is surely our greatest challenge, to assimilate our new learning despite our significant biological limitations. After all, we dont have a great track record in these areas, particularly when you examine the misuse of science and its impact on the ecology of our planet. Increasingly the convergence of quantum science with machine learning will produce AI technology certainly beyond our understanding and possibly beyond our control.

Thousands of well-funded commercial and government agencies are already furiously in competition to make this happen.

After all, this is how we humans have progressed for centuries; we experiment to find how things work and we then apply the new science into our lives, even before we fully understand the consequences of our discoveries.

But if our leaders and politicians are not familiar with the language or alert to the ethical challenges and dangers arising from the application of artificial intelligence and its technologies, then surely we are heading for a potential disaster? Is mise,

Dr VINCENT KENNY

Knocklyon,

Dublin 16.

Read this article:

Artificial intelligence and humankind - The Irish Times

Each year, Samsung Electronics AI Forum brings together experts from all over the world to discuss the latest advancements in artificial intelligence (AI) and share ideas on the next directions for the development of these technologies.

This November 2 and 3, experts, researchers and interested viewers alike convened virtually to share the latest developments in AI research and discussed some of the most pressing and relevant issues facing AI research today.

AI technologies have developed remarkably in recent years, thanks in no small part to the hard work and diverse research projects being done by academic and corporate researchers alike all around the world. But given the rapid and significant changes brought on by the recent global pandemic, attention has recently been turning to how AI can be used to help solve real-life problems, and what methods might be most effective in order to create such solutions.

The first day of the forum, organized by the Samsung Advanced Institute of Technology (SAIT), was opened with a keynote speech by Dr. Kinam Kim, Vice Chairman and CEO of Device Solutions at Samsung Electronics, who acknowledged the importance of the discussions set to take place at this years AI Forum around the past, present and future of the role of AI. Dr. Kim also affirmed Samsung Electronics dedication to working with global researchers in order to develop products and services with meaningful real-world impact.

The first day of the Forum then continued with a series of fascinating invited talks given by several global leading academics and professionals. Professor Yoshua Bengio of University of Montreal, Professor Yann LeCun of New York University and Professor Chelsea Finn of Stanford University were the first three to present, following which the Samsung AI Researcher of the Year awards were presented. After this ceremony, SAIT Fellow Professor Donhee Ham of Harvard University, Dr. Tara Sainath of Google Research and Dr. Jennifer Wortman Vaughan of Microsoft Research gave their talks.

The first days invited talks were followed by a virtual live panel discussion, moderated by Young Sang Choi, Vice President of Samsung Electronics, and attended by Professor Bengio, Professor LeCun, Professor Finn, Dr. Sainath, Dr. Wortman Vaughan and Dr. Inyup Kang, President of Samsung Electronics System LSI business. It is my great pleasure to join this Forum, noted Dr. Kang. I feel as if I am standing on the shoulders of giants.

Questions were given to the panel that invited the experts to discuss the ways in which computational bottlenecks can be overcome in order to take AI systems to the next level and be developed to possess the same intelligibility as the human brain. The panelists weighed the benefits of scaling neural nets as opposed to searching for new algorithms, with Dr. Kang noting that, We have to try both. Given the scale of human synapses, I doubt that we can achieve the human level of intelligibility using just current technologies. Eventually we will get there, but we definitely need new algorithms, too.

Professor LeCun noted how AI research is not just constrained by current scaling methods. We are missing some major pieces to being able to reach human-level intelligence, or even just animal-level intelligence, he said, adding that perhaps, in the near future, we might be able to develop machines that can at least reach the scale of an animal such as a cat. Professor Finn concurred with Professor LeCun. We still dont even have the AI capabilities to make a bowl of cereal, she noted. Such basic things are still beyond what our current algorithms are capable of.

Building on the topic of his invited talk, Professor Bengio added that, in order for future systems to have intelligence comparable to that of the way humans learn as children, a world model will need to be developed that is based on unsupervised learning. Our models need to act like human babies in order to go after knowledge in an active way, he explained.

The panel discussion then moved on to the ways in which the community can bridge the gaps between current technologies and future, human-intelligence level technologies, with all the experts agreeing that there is still much work to be done in developing systems that mimic the way human synapses work. A lot of current research directions are trying to address these gaps, reassured Professor Bengio.

Next, the panel shared their thoughts on how to make AI fairer given the inherent biases possessed by todays societies, with the experts debating the balance that needs to be struck between systems development reform, institutional regulation and corporate interest. Dr. Wortman Vaughan made the case for introducing a diversity of viewpoints across all parts of the system building process. I would like to see regulation around processes for people to follow when designing machine learning systems rather than trying to make everyone meet the same outcomes.

The final question given to the panel asked for their thoughts on which field will be the next successful application area for end-to-end models. End-to-end models changed the field of speech recognition by reducing latency and removing the need for internet connection, noted Dr. Sainath. Thanks to this breakthrough, going forward, youre going to see applications of end-to-end models for such purposes as long meeting transcriptions. We always speak of having one model to rule them all, and this is a challenging and interesting research area that has been expanded by the possibilities of end-to-end models as we look to develop a model capable of recognizing all the languages in the world.

The second day of the AI Forum 2020 was hosted by Samsung Research, the advanced R&D hub of Samsung Electronics that leads the development of future technologies for the companys end-product business.

In his opening keynote speech, Dr. Sebastian Seung, President and Head of Samsung Research, outlined the areas in which Samsung has been accelerating its AI research to the end of providing real-world benefits to their users, including more traditional AI fields (vision and graphics, speech and language, robotics), on-device AI and the health and wellness field.

After showcasing a range of Samsung products bolstered with AI technologies, Dr. Seung affirmed that, in order to best extend the capabilities of AI to truly help people in meaningful ways, academic researchers and corporations need to come together to find best-practice solutions.

Following Dr. Seungs speech, the second day of the Forum proceeded with a series of invited talks around the theme of Human-Centric AI by Professor Christopher Manning of Stanford University, Professor Devi Parikh of the Georgia Institute of Technology, Professor Subbarao Kambhampati of Arizona State University and Executive Vice President of Samsung Research Daniel D. Lee, Head of Samsungs AI Center in New York and Professor at Cornell Tech.

The expert talks were followed by a live panel discussion, moderated by Dr. Seung and joined by Professor Manning, Professor Parikh, Professor Kambhampati and EVP Lee. Dr. Seung kicked off the discussion with a question about a topic raised in Professor Kambhampatis speech around the potential issues that could lead to the risk of data manipulation as AI develops. As AI technology continues to develop, it is important that we stay vigilant about the potential for manipulation and work to solve the issues of any AI systems inadvertent data manipulations, explained Professor Kambhampati.

Dr. Seung then posed a much-requested viewer question to the panel. Given that one of the most practical concerns in AI research is the obtaining of data, the experts were asked whether they believe that companies or academic researchers need to develop new means of handling and managing data. Acknowledging that academics often struggle to secure data while companies possess alleviated data shortage problems yet elevated restraints around the usage of their data, Professor Parikh made a case for the need of new research methods that can be modeled with insufficient data or with cooperation between academia and industry, including open research methods. In many areas, there are big public data sets available, she noted. Researchers outside of companies are able to access and use these. But further to this, some of the most interesting fields in AI today are the ones where we dont have much data these represent some of the most cutting-edge problems and approaches.

The final question took the panel back to the theme of the AI Forums second day, Human-Centered AI, wherein the panelists were asked whether or not they believe that AI will be capable of equaling human intelligence in the next 70 years, since that is the period of time it has taken us to get to where we are today in the field of AI research. EVP Lee reasoned that AI still has a way to go but that 70 years is a long time. I am optimistic, noted EVP Lee, but there are lots of hard problems in the way. We need to have academics and companies working on a goal like this together.

We are currently reaching the limits of the range of problems we can solve using just lots of data, summarized Professor Manning. Before we see AI developments like this on a large scale, an area that we should emphasize is the production of AI systems that work for regular people, not just huge corporations, he concluded.

The Samsung AI Forum 2020 ended with a warm thanks to all the esteemed experts who had taken part in the two-day Forum and a shared hope to hold next years Forum offline. All the sessions and invited talks from the AI Forum 2020 are available to watch on the official Samsung YouTube channel.

More:

Samsung AI Forum 2020: Humanity Takes Center Stage in Discussing the Future of AI - Samsung Global Newsroom

Provizio, a combination hardware and software startup with technology to improve car safety, has closed a seed investment round of $6.2million. Investors include Bobby Hambrick (the founder of Autonomous Stuff); the founders of Movidius; the European Innovation Council (EIC); ACT Venture Capital.

The startup has a five-dimensional sensory platform that it says perceives, predicts and prevents car accidents in real time and beyond the line-of-sight. Its Accident Prevention Technology Platform combines proprietary vision sensors, machine learning and radar with ultra-long range and foresight capabilities to prevent collisions at high speed and in all weather conditions, says the company. The Provizio team is made up of experts in robotics, AI and vision and radar sensor development.

Barry Lunn, CEO of Provizio said: One point three five road deaths to zero drives everything we do at Provizio. We have put together an incredible team that is growing daily. AI is the future of automotive accident prevention and Provizio 5D radars with AI on-the-edge are the first step towards that goal.

Also involved in Provizio is Dr. Scott Thayer and Prof. Jeff Mishler, formally of Carnegie Mellon robotics, famous for developing early autonomous technologies for Google / Waymo, Argo, Aurora and Uber.

Go here to see the original:

Provizio closes $6.2M seed round for its car safety platform using sensors and AI - TechCrunch