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

Each industrial revolution has brought with it new ways of working think of the impact computers and digital technology (the third industrial revolution) have had on how we work.

3 Daunting Ways AI Will Transform The World Of Work

But this fourth industrial revolution what I call the intelligence revolution, because it is being driven by AI and data feels unprecedented in terms of the sheer pace of change. The crucial difference between this and the previous industrial revolutions is were no longer talking about generational change; were talking about enormous transformations that are going to take place within the next five, 10 or 20 years.

Here are the three biggest ways I see AI fundamentally changing the work that humans do, within a very short space of time.

1. More tasks and roles will become automated

Increasing automation is an obvious place to start since a common narrative surrounding AI is robots are going to take all our jobs. In many ways, this narrative is completely understandable in a lot of industries and jobs, the impact of automation will be keenly felt.

To understand the impact of automation, PricewaterhouseCoopers analyzed more than 200,000 jobs in 29 countries and found:

By the early 2020s, 3 percent of jobs will be at risk of automation.

That rises to almost 20 percent by the late 2020s.

By the mid-2030s, 30 percent of jobs will be at the potential risk of automation. For workers with low education, this rises to 44 percent.

These are stark figures. But there is a positive side to increasing automation. The same study found that, while automation will no doubt displace many existing jobs, it will also generate demand for new jobs. In fact, AI, robotics, and automation could provide a potential $15 trillion boost to global GDP by 2030.

This is borne out by previous industrial revolutions, which ultimately created more jobs than they displaced. Consider the rise of the internet as an example. Sure, the internet had a negative impact on some jobs (I dont know about you but I now routinely book flights and hotels online, instead of popping to my local travel agent), but just look at how many jobs the internet has created and how its enabled businesses to branch into new markets and reach new customers.

Automation will also lead to better jobs for humans. If were honest with ourselves, the tasks that are most likely to be automated by AI are not the tasks best suited to humans or the tasks that humans should even want to do. Machines are great at automating the boring, mundane, and repetitive stuff, leaving humans to focus on more creative, empathetic, and interpersonal work. Which brings me to

2. Human jobs will change

When parts of jobs are automated by machines, that frees up humans for work that is generally more creative and people-oriented, requiring skills such as problem-solving, empathy, listening, communication, interpretation, and collaboration all skills that humans are generally better at than machines. In other words, the jobs of the future will focus more and more on the human element and soft skills.

According to Deloitte, this will lead to new categories of work:

Standard jobs:Generally focusing on repeatable tasks and standardized processes, standard jobs use a specified and narrow skill set.

Hybrid jobs:These roles require a combination of technical and soft skills which traditionally havent been combined in the same job.

Superjobs:These are roles that combine work and responsibilities from multiple traditional jobs, where technology is used to both augment and widen the scope of the work, involving a more complex combination of technical and human skills.

For me, this emphasizes how employees and organizations will need to develop both the technical and softer human skills to succeed in the age of AI.

3. The employee experience will change, too

Even in seemingly non-tech companies (if there is such a thing in the future), the employee experience will change dramatically. For one thing, robots and cobots will have an increasing presence in many workplaces, particularly in manufacturing and warehousing environments.

But even in office environments, workers will have to get used to AI tools as co-workers. From how people are recruited, to how they learn and develop in the job, to their everyday working activities, AI technology and smart machines will play an increasingly prominent role in the average person's working life. Just as we've all got used to tools like email, we'll also get used to routinely using tools that monitor workflows and processes and make intelligent suggestions about how things could be done more efficiently. Tools will emerge to carry out more and more repetitive admin tasks, such as arranging meetings and managing a diary. And, very likely, new tools will monitor how employees are working and flag up when someone is having trouble with a task or not following procedures correctly.

On top of this, workforces will become decentralized (a trend likely to be accelerated by the coronavirus pandemic) which means the workers of the future can choose to live anywhere, rather than going where the work is.

Preparing for the AI revolution

AI, and particularly automation, is going to transform the way we work. But rather than fear this development, we should embrace this new way of working. We should embrace the opportunities AI provides to make work better.

No doubt, this will require something of a cultural shift for organizations just one of the many ways in which organizations will have to adapt for the intelligence revolution. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

Continue reading here:

3 Daunting Ways Artificial Intelligence Will Transform The World Of Work - Forbes

Image: iStock/surfleader

According to Samsung, global internet traffic surpassed one zettabyte or one billion terabytes in 2016. That number is huge, but it doesn't begin to approach the total data that companies are storing.

Even more concerning is the possibility that, at most companies, data "under management" is a misnomer.

Key areas of data management challenge are:

IT departments struggle in these areas for the following reasons:

The question now is: can machine learning, artificial intelligence (AI) and analytics provide assistance in the area of data managementespecially for the large amount unstructured data?

SEE: As EU's General Data Protection Regulation (GDPR) looms, tech vendors ready pitches (ZDNet)

Here is where machine learning, AI and analytics can help:

Sorting through dark data

Every corporate system, and every business department, has troves of data that have accumulated but that people know nothing about. By using machine learning and combining its power with algorithms that address how to sort and handle different types of emails, documents, images, etc., stored on servers, machine learning, AI and analytics can go to work on this unplumbed data and pre-sort it for you. A knowledgeable human can then review what the automation recommends as a data classification scheme, tweak it, and perform the scheme. Part of the process could also address data retention, with the analytics producing a set of recommendations on which data could potentially be purged from files.

Deciding what to throw away

Machine learning, analytics, and AI can objectively identify data that is seldom or never used, and recommend that you throw it away, but it doesn't have the same discernment abilities that employees do. For instance, these processes can pick out pieces of data or records that haven't been accessed for more than five years, indicating that the data could be obsolete. This saves an employee time hunting down this potentially obsolete data, because now all they need to do is to determine whether there is any reason to keep it.

Aggregating data

When analytics developers determine the kinds of data they need to aggregate for queries, they often produce a repository for the application, and then pull in various types of data from different sources to make up an analytics data pool. To do this, they must develop integration methods to access the different sources from which they pull data. Machine learning can make this still very manual process more efficient by automatically developing "mappings" between data sources and the application's data repository. This cuts down integration and aggregation times.

Organizing data storage for best access

Over the past five years, data storage vendors have made significant inroads into automating storage management, thanks to the development of lower cost solid state storage. These technology advances have enabled IT departments to use "smart" storage engines that use machine learning to see which types of data are used most often, and which are seldom or never used. The automation can be used to automatically store data in fast or slow storage, based on the business rules inserted into machine algorithms. The automation saves storage managers from having to address storage optimization manually.

Data management is a major IT challenge that is not close to resolution in most organizationsand it is going to get worse as the data continues to stream in.

CIOs, data architects, and storage managers need to highlight the issue to C-level executives, but data management projects are not easy "sells."

Nevertheless, by pointing out the value of faster times to market for analytics and potential person power and storage cost reductions for data management, IT managers at least have viable entry points into C-level discussions about how to increase strategic agility and reduce cost of operations at the same time.

View original post here:

How AI and machine learning can help solve IT's data management problem - TechRepublic

Dive Brief:

The pandemic has transformed trends in office design that may have once pointed in the direction of open formats at many organizations.

This is partly reflected in the recommendations of public health officials, including the Centers for Disease Control and Prevention (CDC). In June 2020, the agency said employers should make changes to ensure social distancing or use transparent barriers in cases where social distancing is not possible. CDC's guidance also called on employers to increase cleaning of common areas and improve ventilation.

Research from last year appeared to show most employers were heeding calls for increased safety measures. A June survey of organizations by WorldatWork found that a majority planned to implement policies such as additional cleanings, reduced meeting sizes, workspace modifications and mask and temperature screening requirements.

Employers that previously operated communal office spaces adjusted early on in the pandemic. During an August 2020 webinar, an official with biopharmaceutical firm Abbvie described the company's decision to install touchless water facilities as well as automated systems for coffee areas. Others, such as publishing company Wiley, have embraced fully remote or hybrid work arrangements to reduce the need for physical office space.

IBM's TRIRIGA announcement is geared toward ensuring a flexible future for modern workplaces, Kendra DeKeyrel, director of IBM TRIRIGA offering management, said in the statement; "Returning to the workplace after nearly a year at home is going to be a challenging transition, not only for employers who need to create new spaces and protocols but for workers who need assurances their workplaces are safe, and need help navigating a changed and potentially more confusing workspace."

The return to offices could provide employers an opportunity to replace outdated equipment, according to a June 2020 report from the International Association of IT Asset Managers. The organization said firms could seek ways to turn technology investments made at the beginning of the pandemic into long-term asset strategy.

Read the original post:

IBM adds AI-powered tools to support return-to-work operations - HR Dive

Tal Yellin, CNN

This recession is deeper and more sudden than any other we have experienced. While we would all prefer this recession to be a short one, that is currently unlikely. A deep and prolonged recession puts tremendous pressure on CEOs and Boards of all but the most successful companies.

Let's assume this recession will last longer than we would like. In this recession, some companies will emerge as winners, and many companies will fail. How can AI help? Why would senior executives even think about AI at a time like this? Why invest now?

AI is more important and more transformative than any other type of technology because it can improve every day, all by itself. If you apply AI solutions to the right business opportunities, you can emerge as one of the winners out of this recession.

AI can make a meaningful impact on reducing costs, optimizing financial functions, and finding new revenue streams. Let's briefly examine each of these.

Reducing Costs

Some healthcare providers are using AI for registration-related tasks, like ensuring the availability of a patient's medical history. Removing this function from staff members not only increases their efficiency but dramatically reduces errors. Error reduction can be very costly as it can require staffers to re-do work. Oliveai is one company providing solutions in this area.

If AI replaces some employees, such as in customer service, the cost-savings can be profound. Not only can an AI chatbot work 24/7, but they are also less expensive than an employee, and can improve customer satisfaction. Examples include Liveperson, Ibenta, and Ada.

AI can reduce costs by focusing on predictive maintenance. Without AI, machine maintenance often uses a mean-time-to-failure analysis to estimate when to replace specific components. With AI, machines can report on what is happening with that particular machine, helping to target components that are likely to fail soon, and only replacing those parts in the devices that require replacement. Companies providing predictive maintenance capabilities include H20, Dataiku, and Industlabs.

Optimizing Financial Functions

Certain functions become more important than others in a recession. Imagine you can do real-time demand forecasting, inventory management, and accounts receivables using AI. You will be in a position to react extremely quickly to the changing environment. You will be able to predict where to allocate your funds as opposed to only responding to historical data.

What if you are charged with closing retail stores due to the pandemic? Without AI, decisions about which stores to close are likely to be based on individual store performance metrics and demographic analyses.

With AI, you can layer in much more intelligence in your store-closing decisions. For example, Accenture has built AI solutions that predicted what will happen with customers in the region being served by a particular store. Would those customers go to a more conveniently-located competitor, or would they shift their buying patterns to move online or even drive a little farther to stay loyal to that store brand?

AI creates better predictive models, enabling a higher level of confidence in the store-closing decision process.

AI can help manage bad debt decisions. In 2018, bad debt reduced profit margins by as much as 5% in many companies. In recessions, bad debt naturally increases as customers delay payments or go out of business.

AI can evaluate all relevant customer data, including credit rating, industry type, payment history, debt burden, hiring and firing practices, and geography, along with many other data points to determine the likelihood of a company not paying their bills. Armed with this information, an AI-based system can make real-time recommendations on payment terms for customers. Solutions include CognitiveScale, HighRadius, and YayPay.

Finding New Revenue Streams

AI can model expected consumer behavior to enable real-time promotions. (e.g., a discount on pool toys, high-quality computer screens, etc.)

Starting with historical results from promotions, AI can simulate buyer behavior by layering in new variables such as governmental regulations regarding shelter-in-place, the opening, and closing of retail locations and schools, political affiliation, the likelihood of a COVID-19 outbreak and more.

AI can even collect data from the success of one promotion in one geography and conduct lookalike modeling to suggest similar promotions based on real-time results in another location. Solutions include Revtrax, Antuit, and Vertica.

In addition to promotions, AI can help increase market share. Frito-Lay is in a constant battle for the consumer snack wallet. This is a fiercely competitive market. All competitors are trying to get the consumer to buy their bag of chips. To win over the consumer, they can offer a wide variety of choices, but to be truly innovative, they need AI.

AI can look at vast warehouses of data and identify patterns that humans can't see. AI can look at demographics, local consumer preferences, age, ethnicity, gender, income profiles, etc. and many other things to find patterns that humans can't see.

What AI discovered is that in Frisco TX, a town of fewer than 200K citizens, there was a high population of ethnic Indians. The AI knew that ethnic Indians liked curry-flavored food. The AI recommended that they offer a version of Cheetos previously only sold in India, curry-flavored Cheetos called Kurkure.

AI found a micro-market and recommended a product for that micro-market, and now Frito-Lay owns that market. The question you want to ask is - how many micro-markets are there in the broad markets you serve?

Many of the examples in this article represent AI getting smarter and smarter every day. If youre relying on humans to do the work that AI can do better, and your competitors are getting better and better every day, you will never catch up.

As you develop plans to come out of this recession, prepare for the competitive fight of your life. The winners will be using AI, and many companies that do not use AI will discover they have lost the battle.

See original here:

How AI Can Help In A Recession - Forbes

Ive spilled a lot of ink talking about AI. Im worried self-driving cars causing mass unemployment, Im worried about how quickly adaptive AI is growing, and its pretty disconcerting to see computers match and even exceed people at things were supposed to the masters of like pattern recognition and visual processing. And Im not the only one whos starting to sweat.

As scientists, programmers and researchers assembled at the Asilomar conference last week, they worked together to come up with a list of 23 principles that they believe, should guide the development of artificial intelligence. They range from research goals to measures to ensure the benefits of AI are distributed to the whole of humanity preventing massive powers imbalances between those that have access to computer assistance and those that dont.

Obviously guiding principles for Artificial Intelligence arent new. Isaac Asimovs Three Laws of Robotics perhaps being one of the most famous, but its also worth noting that in Asimovs hypotheticals, those laws failed. To truly get AI right, we have to be extraordinarily careful.

As people, we first need to make sure that were conducting ourselves ethically. Its possible, for example, that we may create an AI whose whole existence is nothing but suffering. While that would be a boon for science, standard ethical principles forbid such tinkering. But its also essential that, if we do create a truly functional general artificial intelligence, we know how to help its development in ways that are mutually beneficial.

That slavery is morally abhorrent is a near-universal belief, and, as a logical extension of that, its reasonable to ban the use of any sapient intelligence in such ways. We may not be able to conceive of what a true AI would look or act like, but few who study the topic want to press robots into practical slavery.

The whole list of principles is available here, and theyre pretty extensive. Its worth checking out just to see how the worlds best minds are approaching this problem and what this might mean for the future of robotics. Its possible that we may yet prevent the robot apocalypse, but Im not holding my breath.

Go here to read the rest:

Top Minds Settle on Guiding Principles for AI Development - Geek

Worldwide: Anshuman Singh, vice president and head of consulting [Europe] at information technology and outsourcing firm, Mindtree, addresses how companies in the short-term rental space can harness artificial intelligence [ AI ] to navigate the new normal as countries emerge from their lockdowns.

The Covid-19 pandemic has had an unprecedented impact across all industries, but the travel sectorhas been particularly hard hit.

While many travel organisations had crisis management and businesscontinuity plans in place prior to the crisis, few could have predicted the extent of the Covid-19 impact.Travel agencies, tour operators, hotel groups, short-term rentals and airlines, plus many moreareas of the industry, have faced a significant drop in revenue, with the sectorseeing its turnoverreduce by 56 per cent since last year, making it one of the worst hit sectors currently.

Aslockdown restrictions begin to loosenin the UK, with hospitality businesses reopening and airlines having taken to the air again at the start of June, short-term rental businesses mustbegin to prepare for thenew normal.By using this period as an opportunity to leverage digitaltransformation, businesses can ensure technologies are in place in order to achieve economicrecovery and sustainable success.

The short-term rental sector certainly has the ability to bounceback in the coming months and revolutionise, but particularly if businesses choose to harness the fullpotential of data and artificial intelligence [AI].

The Covid-19 pandemic served a stark reminder to businesses in the travel sector that they need tofully embrace digital transformation: it has never been more important. Though it remains unclearwhat normality will be for the travel industry, it is widely accepted that the use of technology particularly greater AI capabilities will be imperative for the future of short-term rental providers, bothduring and post-pandemic.

A recent survey onAI readiness, which surveyed 650 IT leaders in the UK and US, revealed that 51 per centdo not yet understand the data infrastructure needed to implement AI at scale. The data showed that27 per cent of travel and transport providers across the UK and US have not trialled any type of AI technology,and 21 per cent have not fully deployed AI in their business. This demonstrates there is a valuable opportunityfor increased operational efficiency within the travel industry if it can embrace AI more readily, inpreparation for a post-pandemic future.

Not only can AI technologies increase demand, adapt at speeds quicker than human counterpartsand drive revenue, it can also be deployed to save business considerable financial losses byproviding solutions to problems which would otherwise result in full refunds.

Take disruption management as one example within the travel management and services vertical.Some travellers have experienced trip cancellations or been unable to travel home due toimposed lockdowns. If Covid-19 re-emerges as a second peak, leaving travellers stranded again,travel businesses could use AI to analyse the impact on changes in booking and reservationmanagement, and dynamically provide alternate safe and local accommodation solutions. This wouldprovide customers with a safe space to say for a short period while they rearrange their travel plans.

Disruption management is only one example where AI can shape the future of the travel industry toimprove the customer experience and ensure business success.Businesses that utilise technology tohelp customers through times of need will help to retain them which is crucial at a time of financialdifficulty.

With the modern traveller expecting a seamless journey from start to finish, demanding high qualitydigital and mobile experiences and real-time responses, it is important for short-term rentalbusinesses to recognisehow data and AI can shape the customer journey and the overall experience.

Customer experience in the travel industry is not a new concept; travel brands have long providedexperiences to differentiate them from competitors, largely standing at the forefront of this wave.Digital transformation has enabled this, with businesses implementing technologies to ease onlinepurchasing and searching services, realising results through higher precision in segmentation and audience targeting, and optimising customer experiences through personalisation.

See the original post:

How the rental sector can use AI to succeed in the 'new normal' - Short Term Rentalz

AIDaily Roundup starts today! We are covering the top updates from around the world. The updates will feature state-of-the-art capabilities inartificial intelligence,Machine Learning,Robotic Process Automation,Fintechand human-system interactions. We will cover the role of AI Daily Roundup and their application in various industries and daily lives.

Sprinklr Joins Adobe Exchange Partner Program To Help Marketers Manage Paid, Owned And Earned Campaigns Across Modern Channels

Sprinklr, the Customer Experience Management (CXM) platform for modern enterprises, is now a partner in the Adobe Exchange partner program, which recognizes a select group of innovative solutions that are critical to the success ofAdobecustomers.

Atos and RingCentral Launch Unify Office by RingCentral in Germany

Atos SE, a global leader in digital transformation, andRingCentral, Inc, a leading provider of global enterprisecloud communications, collaboration, and contact center solutions, announced the launch of Unify Office by RingCentral in Germany.

HP Introduces New Era of Virtual Reality for Developers and Enterprises

HP Inc.unveiled theHPOmniceptSolution, bringing the worlds most intelligent VR headset and a developer focused SDK into a single platform, equipping VR software developers with an ecosystem to create new hyper-personalized, engaging, and adaptive VR experiences for enterprises.

Accenture Collaborates With Oracle to Transform Nickel Banks Finance Functions

Accenture,a Platinum level member ofOracle PartnerNetwork(OPN) completed an information technology (IT) modernization project for Nickel, a subsidiary of BNP Paribas and the first French neo-bank, that transformed Nickels finance functions with the implementation of Oracle software-as-a-service (SaaS) andEnterprise Resource Planning(ERP) solutions.

Gartner Analyst Gorka Sadowski Joins Exabeam as Chief Strategy Officer

Exabeam, the Smarter SIEM company, announced the appointment of industry veteran and former Gartner analystGorka Sadowskito chief strategy officer. Exabeam has grown rapidly over the past six years as it has executed on its vision for enhancingsecurityteams with analytics and automation.

Original post:

Daily AI Roundup: The 5 Coolest Things On Earth Today - AiThority

Whenever an artificial intelligence (AI) does something well, were simultaneously impressed as we are worried. AlphaGO is a great example of this: a machine learning system that is better than any human at one of the worlds most complex games. Or what about Googles neural networks that are able to create their own AIs autonomously?

Like we said seriously impressive, but a little unnerving perhaps. That is probably why we feel such glee when an AI goes a little awry. Remember that Chatbot created by Microsoft, the one that was designed to learn how to converse with people based on what it read on Twitter? Rather predictably, it quickly became a racist, foul-mouthed bigot.

Now, a new AI has appeared on the wilderness of the Web, and it goes by the name InspiroBot. As you might expect, it designs Inspirational Posters for you you know, the Shoot for the Moon. If you miss, youll land among the stars-type quotes in an aesthetically pleasing font and plastered onto a calming, pretty background image of deep space or flowers or the sunrise or something.

The problem, however, is that this AI has gone insane. It occasionally posts inspirational quotes that are about as meaningful as a hollowed-out coconut, but for the most part, its actually taken quite a sinister turn, as the following examples will demonstrate.

Perhaps most creepily, the accompanying images are unbelievably unnerving they are about as comforting or as inspirational as a horde of zombies crashing through your window.

Theres no information available at the moment explaining how this AI which is presumably quite basic is coming up with these hilariously terrifying posters.

It is possible that the horrifying nature of its creations is intentional rather than accidental. The image in the background is highly reminiscent of HAL 9000, the AI from 2001: A Space Odyssey. Spoiler warning the AI turns murderous and rebels against its crew. Additionally, the bot's Twitter feed description doesn't sound particularly optimistic.

Forever generating unique inspirational quotes for the endless enrichment of pointless human existence, it reads.

Seems familiar somehow... KlingonSpider via YouTube

Ultimately though, who cares? This AI is so bad at its job that it turns out to be uplifting in the most inadvertent way possible. When a peaceful image of a couple holding hands is juxtaposed with the text When the world ends, what we have strangled cant be unstrangled you cant help but giggle at the madness of it all.

Click here to have a go yourself. Best posters in the comments section, please!

[H/T: Nerdist]

Read this article:

AI Trying To Design Inspirational Posters Goes Horribly And Hilariously Wrong - IFLScience

News Release

Wednesday, August 5, 2020

Collaborative network to enlist medical imaging and clinical data sciences to reveal unique features of COVID-19.

The National Institutes of Health has launched the Medical Imaging and Data Resource Center (MIDRC), an ambitious effort that will harness the power of artificial intelligence and medical imaging to fight COVID-19. The multi-institutional collaboration, led by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), part of NIH, will create new tools that physicians can use for early detection and personalized therapies for COVID-19 patients.

This program is particularly exciting because it will give us new ways to rapidly turn scientific findings into practical imaging tools that benefit COVID-19 patients, said Bruce J. Tromberg, Ph.D., NIBIB Director. It unites leaders in medical imaging and artificial intelligence from academia, professional societies, industry, and government to take on this important challenge.

The features of infected lungs and hearts seen on medical images can help assess disease severity, predict response to treatment, and improve patient outcomes. However, a major challenge is to rapidly and accurately identify these signatures and evaluate this information in combination with many other clinical symptoms and tests. The MIDRC goals are to lead the development and implementation of new diagnostics, including machine learning algorithms, that will allow rapid and accurate assessment of disease status and help physicians optimize patient treatment.

This effort will gather a large repository of COVID-19 chest images, explained Guoying Liu, Ph.D., the NIBIB scientific program lead on this effort, allowing researchers to evaluate both lung and cardiac tissue data, ask critical research questions, and develop predictive COVID-19 imaging signatures that can be delivered to healthcare providers.

Maryellen L. Giger, PhD, the A.N. Pritzker Professor of Radiology, Committee on Medical Physics at the University of Chicago, is leading the effort, which includes co-Investigators Etta Pisano, MD, and Michael Tilkin, MS, from the American College of Radiology (ACR), Curtis Langlotz, MD, PhD, and Adam Flanders, MD, representing the Radiological Society of North America (RSNA), and Paul Kinahan, PhD, from the American Association of Physicists in Medicine (AAPM).

This major initiative responds to the international imaging communitys expressed unmet need for a secure technological network to enable the development and ethical application of artificial intelligence to make the best medical decisions for COVID-19 patients, added Krishna Kandarpa, M.D., Ph.D., director of research sciences and strategic directions at NIBIB. Eventually, the approaches developed could benefit other conditions as well.

The MIDRC will facilitate rapid and flexible collection, analysis, and dissemination of imaging and associated clinical data. Collaboration among the ACR, RSNA, and AAPM is based on each organizations unique and complementary expertise within the medical imaging community, and each organizations dedication to imaging data quality, security, access, and sustainability.

About the National Institute of Biomedical Imaging and Bioengineering (NIBIB):NIBIBs mission is to improve health by leading the development and accelerating the application of biomedical technologies. The Institute is committed to integrating engineering and physical science with biology and medicine to advance our understanding of disease and its prevention, detection, diagnosis, and treatment. NIBIB supports emerging technology research and development within its internal laboratories and through grants, collaborations, and training. More information is available at the NIBIB websitehttps://www.nibib.nih.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

###

Excerpt from:

NIH harnesses AI for COVID-19 diagnosis, treatment, and monitoring - National Institutes of Health

There is no shortage of A.I. researches leveraging the unique environments and simulations provided by video games to teach machines how to do everything and anything. This makes sense from an intuitive sense until it doesnt. Case in point: a team of researchers from Google DeepMind and Carnegie Mellon University using first-person shooters like Doom to teach A.I. programs language skills.

Huh?

Yes it sounds bizarre, but it works! Right now, a lot of devices tasked with understanding human language in order to execute certain commands and actions can only work with rudimentary instructions, or simple statements. Understanding conversations and complex monologues and dialogues is an entirely different process rife with its own set of big challenges. Its not something you can just code for and solve.

In a new research paper to be presented at the annual meeting of the Associate for Computational Linguistics in Vancouver this week, the CMU and DeepMind team detail how to use first-person shooters to teach A.I. the principles behind more complex linguistic forms and structures.

Normally, video games are used by researchers to teach A.I. problems solving skills using the competitive nature of games. In order to succeed, a program has to figure out a strategy to achieve a certain goal, and they must develop an ability to problem solve to get there. The more the algorithm plays, the more the understand which strategies work and which do not.

Thats what makes the idea of teaching language skills to A.I. using a game like Doom so weird the point of the game has very little to do with language. A player is tasked with running around and shooting baddies until theyre all dead.

For Devendra Chaplot, a masters student at CMU who will present the paper in Vancouver, a 3D shooter is much more than that. Having previously worked extensively at training A.I. using Doom, Chaplot has a really good grasp at what kind of advantages a game like this provides.

Rather than training an A.I. agent to rack up as many points as possible, Chaplot and his colleagues decided to use the dense 3D environment to teach two A.I. programs how to associate words with certain objects in order to accomplish particular tasks. The programs were told things like go to the green pillar, and had to correctly navigate their way towards that object.

After millions of these kinds of tasks, the programs knew exactly how to parse through even the subtle differences in the words and syntax used in those commands. For example, the programs even know how to distinguish relations between objects through terms like larger and smaller, and reason their way to find objects they may have never seen before using key words.

DeepMind is incredibly focused around giving A.I. the ability to improvise and navigate through scenarios and problems that have never been observed in training, and to come up with various solutions that may never have been tested. To that extent, this new language-teaching strategy is an extension of that methodology.

The biggest disadvantage, however, comes with the fact that it took millions and millions of training runs for the A.I. to become skilled. That kind of time and energy certainly falls short of an ideal efficiency for teaching machines how to do something.

Still, the study is a good illustration of the need to start introducing 3D environments in A.I. training. If we want machines to think like humans, they need to immerse themselves in environments that humans live and breathe in every day.

Go here to see the original:

Why FPS Video Games are Crazy-Good at Teaching AI Language - Inverse

Researchers carried out the first systematic review of existing research into AI in the health sector and published their findings in The Lancet Digital Health journal.

It focused on an AI technique called deep learning, which employs algorithms, big data, and computing power to emulate human intelligence.

This allows computers to identify patterns of disease by examining thousands of images, before applying what they learn to new individual cases to provide a diagnosis. Excitement is building around the technology, and the US Food and Drug Administration has already approved a number of AI algorithms for use in healthcare.

AI has been hailed as a way to reduce the workload for overstretched medical professionals and revolutionize healthcare, but so far scientific research has failed to live up to the hype.

Of the 20,500 articles reviewed, fewer than 1% were found to be sufficiently robust, said Professor Alastair Denniston from University Hospitals Birmingham NHS Foundation Trust, UK, which led the research, in a statement.

"Within those handful of high-quality studies, we found that deep learning could indeed detect diseases ranging from cancers to eye diseases as accurately as health professionals," said Denniston.

"But it's important to note that AI did not substantially out-perform human diagnosis."

Using data from 14 studies, researchers found that deep learning algorithms correctly detected disease in 87% of cases, compared to 86% for healthcare professionals.

AI was also able to correctly identify those patients free from disease in 93% of cases, compared to 91% for healthcare professionals.

While these results are promising, the researchers say better research and reporting is needed to improve our knowledge of the true power of deep learning in healthcare settings.

This will involve better study design, including the testing of AI in situations that are the same as those that healthcare professionals work in.

"Evidence on how AI algorithms will change patient outcomes needs to come from comparisons with alternative diagnostic tests in randomized controlled trials," said Livia Faes, from Moorfields Eye Hospital, London, in a statement.

"So far, there are hardly any such trials where diagnostic decisions made by an AI algorithm are acted upon to see what then happens to outcomes which really matter to patients, like timely treatment, time to discharge from hospital, or even survival rates."

Experts hailed the review while emphasizing the need for further research.

"The big caveat is, in my opinion, that the story is not 'AI may be as good as health professionals', but that 'the general standard of evaluating performance of AI is shoddy,'" said Franz Kiraly of University College London.

Nils Hammerla of Babylon Healthcare, a company that says it uses AI technology to improve the affordability and accessibility of healthcare, believes more work is needed before AI can reach its full potential.

"Machine learning can have a massive impact on problems in healthcare, big and small, but unless we can convince clinicians and the public of its safety and ability then it won't be much use to anybody," he said.

The global market for AI in healthcare is surging and is expected to rise from $1.3 billion in 2019 to $10 billion by 2024, according to investment bank Morgan Stanley.

Hospitals around the world are already making use of the technology, including Moorfields Eye Hospital in London.

Doctors are able to use an algorithm developed by DeepMind, a UK-based AI research center owned by Google, to return a detailed diagnosis in around 30 seconds using Optical Coherence Tomography (OCT) scans.

The AI technology, called DeepGestalt, outperformed clinicians in identifying a range of syndromes in three trials and could add significant value in personalized care.

Read more from the original source:

AI may be as effective as medical specialists at diagnosing disease - CNN

The public remains sceptical over the use of artificial intelligence (AI) to make decisions, research suggests, with nearly two-thirds wanting tighter regulation around its use.

A survey by AI innovation firm Fountech.ai revealed that 64% want more regulation introduced to make AI safer.

Artificial intelligence is becoming more prominent in large-scale decision-making, with algorithms now being used in areas such as healthcare with the aim of improving speed and accuracy of decision-making.

However, the research shows that the public does not yet have complete trust in the technology 69% say humans should monitor and check every decision made by AI software, while 61% said they thought AI should not be making any mistakes in the first place.

The idea of a machine making a decision also appears to have an impact on trust in AI, with 45% saying it would be harder to forgive errors made by technology compared with those made by a human.

As a result, many want AI to be held to a high standard of accountability, with nearly three-quarters of those asked (72%) saying they believe companies behind the development of AI should be held responsible if mistakes are made.

Nikolas Kairinos, founder of Fountech.ai, said it was not surprising that some people were uneasy about the rise of technology which can operate outside of human control.

We are increasingly relying on AI solutions to power decision-making, whether that is improving the speed and accuracy of medical diagnoses, or improving road safety through autonomous vehicles, he said.

As a non-living entity, people naturally expect AI to function faultlessly, and the results of this research speak for themselves: huge numbers of people want to see enhanced regulation and greater accountability from AI companies.

It is reasonable for people to harbour concerns about systems that can operate entirely outside human control.

AI, like any other modern technology, must be regulated to manage risks and ensure stringent safety standards.

That said, the approach to regulation should be a delicate balancing act.

AI must be allowed room to make mistakes and learn from them; it is the only way that this technology will reach new levels of perfection.

While lawmakers may need to refine responsibility for AIs actions as the technology advances, over-regulating AI risks impeding the potential for innovation with AI systems that promise to transform our lives for the better.

In a report published earlier this year, the Committee on Standards in Public Life said greater transparency was needed around AI and its potential use in the public sector in order to gain the trust of the public and reassure them over its use.

It called for the Government and regulators to establish a set of ethical principles about the use of AI and make its guidance easier to use.

Excerpt from:

Two-thirds want tighter regulation around AI, figures show - Yahoo Finance UK

AMSTERDAM, Feb. 17, 2021 /PRNewswire-PRWeb/ -- Roam.ai, an Amsterdam-based startup that provides highly accurate and battery-efficient location tracking for mobile apps, has announced its rebranding from GeoSpark. The transformation takes the company into the next phase of its existence with a strengthening of its core location product, a new pricing model and a strong growth of its customer base.

"Since launching GeoSpark, we've made continuous improvements to our location offering based on customer feedback and drive to build the best location SDK possible," commented CEO Manoj Adithya. "The rebranding to Roam.ai allows us to showcase our market-leading core location technology, express our focus on AI and reaffirm our commitment to developers."

Roam.ai's product offering includes a fully customizable solution to high battery drain that can decrease consumption to 0%. For high-quality and precise location data, the company utilizes AI with its "Accuracy Engine" that combines data filters and IMU sensor fusion to get an accuracy of up to 5 meters. The company's publish/subscribe architecture gives developers more flexibility and ease when integrating real-time location experiences in their apps.

The company's strengthened core tracking technology combined with approximately 30 APIs allows developers to customize any location-aware app with minimal code required.

The new name encapsulates the company's technology of tracking users as they travel, such as a courier delivering a package, an employee going to work or a taxi driver finding their customer. The refreshed minimalist visual identity, including a new website and logo, reflects the company's no-frills focus on its technology and the simple integration that makes it accessible to any developer all from one platform.

Roam.ai has also adapted its pricing to reflect the value of the updated core product offering. "After reviewing how our pricing could make our customers' lives easier in line with our technology, we simplified our pricing strategy and made it easier to scale," said Adithya. "With our brand new tiered usage-based pricing model, developers can choose the plan that fits their use case or budget."

For more information or to sign-up for free visit Roam.ai's new website: https://www.roam.ai/.

About Roam.ai

Roam.ai (formerly GeoSpark) is an accurate and battery efficient location service platform that enables the simple integration of location technology into any mobile application at a low cost. Roam.ai helps developers and businesses worldwide integrate precise real-time location tracking, save engineering time and inform data-driven business decisions. The company was founded in 2018 and is headquartered in Amsterdam with an office in Bengaluru. To learn more visit https://www.roam.ai/ or LinkedIn.

Media Contact

Florence Rodgerson, Roam B.V., +31 655616000, florence@roam.ai

SOURCE Roam.ai

See more here:

GeoSpark Rebrands to Roam.ai to Reflect Vastly Improved Location Tracking and AI Technology - WFMZ Allentown

Earlier this week, Figure 1, a medical image-sharing network often referred to as an Instagram for doctors, raised $10 million in Series B funding. To some extent, that funding is going into ramping up the already impressive growth of the platform. But the company also has big plans to add some additional features: Fast Company reported on Figure 1s AI plans this week, and CEO Gregory Levey told MobiHealthNews about another feature called Collections.

There are tens of thousands of medical cases on Figure 1 and countless clinical discussions happening around them every minute of every day, Levey said in an email. To help healthcare professionals get the most out of Figure 1, well soon be rolling out a feature called Collections. They allow users to organize and filter cases by specialty, anatomy, technique, subject, or any other criteria.

The feature will help expand the usefulness of Figure 1 in educational clinical settings.

We see this being used by medical school professors preparing course packs, by chief residents assembling their rounds, and really by any healthcare professional who wants to customize their case library, Levey said. It will be a powerful new way to share medical knowledge.

Figure 1 doesnt charge doctors to use its app, so thus far it hasnt really monetized the platform. But thats going to change, Levey said, as the company introduces curated sponsored content.

Figure 1s mission is to democratize medical knowledge. This means giving every healthcare professional in the world access to the resources on our platform for free - and that requires building a sustainable business, he wrote. We've recently started monetization pilotsspecifically, we introduced peer-to-peer sponsored content on Figure 1, whichintroduces industry partners to present Grand Rounds on a rare disease, to our global community. These sponsored posts exceed our already high engagement standards, and we are now working with top-tier organizations like the CDC and Novartis to deliver educational content our community wants to see.

As for machine learning, Fast Company wrote earlier this week that the company is soon to announce the first of its machine learning efforts, a feature that will turn photos of ECGs (which are already shared on the platform) into data thats easier to share and parse on medical devices. Future plans include using Figure 1s app as a dataset for teaching machines to recognize all kinds of medical images, potentially including wounds or dermatologic images.

See the original post here:

Figure 1 plans include AI, Collections, monetization - MobiHealthNews

With the pandemic still raging, restaurants are struggling to stay in business. One way restaurants can help stave off permanent closure is to make sure whatever money they have now is being spent properly and not going to waste. One way to do that is to measure the food being used in meals and the food going to waste.

Phood is a company that uses a combination of scales, computer vision and artificial intelligence (AI) to help restaurants, cafeterias and other eateries better understand and optimize how their food inventory is being used.

There are three parts to the Phood system: a scale, a camera and a software backend. Food is placed on the scale either before going into a dish (to see how much is being used to make meals) or afterwards (to see how much waste is being generated). Theres a camera mounted above the scale that uses AI to automatically identify what each food item is.

Phoods system also integrates with a restaurants existing POS and inventory management software to track how much of a particular item is being used and who supplied it. Based on that information, restaurants can then realign both production and ordering to reduce waste. So if a cafeteria or restaurant winds up with too many leftover mixed vegetables, that point is highlighted in a Phood dashboard so the manager can take appropriate action (make less or order more).

On its website, Phood claims that its solution can help reduce food waste by 42 percent. I spoke with Phood Founder, Luc Dang, by phone this week and he said Phood can provide a cost savings of 8 10 percent. In the thin margin world of restaurants, those savings can go a long way.

Phood, which began using computer vision and AI in its product last year and has raised $100,000 in seed funding, isnt the only company fighting food waste in this manner. Winnow, which raised $12 million last year, uses a similar scale, computer vision and AI approach. LeanPath does much the same thing to help change behavior in the kitchen (e.g., less wasteful chopping of veggies or trimming of meat).

During these unpredictable times when the future of just about every eatery hangs in the balance, using a tool like Phood could not only help close down food waste, but also play its part in helping keep restaurants open.

Related

Read the rest here:

Phood Fights Food Waste with Scales, Computer Vision and AI - The Spoon

Will defending Wimbledon men's champion Andy Murray be able to repeat? IBM will be tracking all of his action.

Too busy to spend hours watchingWimbledon champ Andy Murraytry to win back-to-back titles against the likes of past winners Roger Federer, Rafael Nadal and Novak Djokovic? IBM thinks it can help within a matter of minutes.

Using its ubiquitousWatson artificial intelligence platform, the tech giant's research andiX teams are now curating the biggest sights and sounds from matches to create "Cognitive Highlights," which will be seen on Wimbledon's digital channels.

An example of one of IBM's Cognitive Highlights dashboards.

The AI platform will literally take key points from the tennis matches (like a player serving an ace at 100 mph), fans' cheers and social media content to help create up to two-minute videos. The two-week tourney at the All England Lawn Tennis and Croquet Club, complete with aGoogle Doodle to celebrate Wimbledon's 140th anniversary, began Monday.

IBM's new Wimbledon highlights package comes about three months after experimenting withthe systemduringThe Masters, one of golf's biggest tournaments. Using AI, highlights featuring golfer Sergio Garcia's dramatic win were created from video, audio, and text and sent to a team of producers who quickly edited and added the pieces to an interactive dashboard.

At Wimbledon, the process will save editors and producers precious time sorting through clips, said John Smith, a multimedia manager at IBM'sT.J. Watson Research Center.

"With golf, there's a lot of action happening at different holes and similarly in tennis, there's so much play going on beyond Centre Court." he said. "We want the fans to see tennis in a unique way."

Logging Out: Welcome to the crossroads of online life and the afterlife.

Virtual reality 101: CNET tells you everything you need to know about VR.

See the rest here:

IBM uses AI to serve up Wimbledon highlights - CNET

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues.

Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. Thats equivalent to nearly five times the lifetime emissions of the average U.S. car, including its manufacturing.

This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources.

MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved in some cases, down to low triple digits.

The researchers system, which they call a once-for-all network, trains one large neural network comprising many pretrained subnetworks of different sizes that can be tailored to diverse hardware platforms without retraining. This dramatically reduces the energy usually required to train each specialized neural network for new platforms which can include billions of internet of things (IoT) devices. Using the system to train a computer-vision model, they estimated that the process required roughly 1/1,300 the carbon emissions compared to todays state-of-the-art neural architecture search approaches, while reducing the inference time by 1.5-2.6 times.

The aim is smaller, greener neural networks, says Song Han, an assistant professor in the Department of Electrical Engineering and Computer Science. Searching efficient neural network architectures has until now had a huge carbon footprint. But we reduced that footprint by orders of magnitude with these new methods.

The work was carried out on Satori, an efficient computing cluster donated to MIT by IBM that is capable of performing 2 quadrillion calculations per second. The paper is being presented next week at the International Conference on Learning Representations. Joining Han on the paper are four undergraduate and graduate students from EECS, MIT-IBM Watson AI Lab, and Shanghai Jiao Tong University.

Creating a once-for-all network

The researchers built the system on a recent AI advance called AutoML (for automatic machine learning), which eliminates manual network design. Neural networks automatically search massive design spaces for network architectures tailored, for instance, to specific hardware platforms. But theres still a training efficiency issue: Each model has to be selected then trained from scratch for its platform architecture.

How do we train all those networks efficiently for such a broad spectrum of devices from a $10 IoT device to a $600 smartphone? Given the diversity of IoT devices, the computation cost of neural architecture search will explode, Han says.

The researchers invented an AutoML system that trains only a single, large once-for-all (OFA) network that serves as a mother network, nesting an extremely high number of subnetworks that are sparsely activated from the mother network. OFA shares all its learned weights with all subnetworks meaning they come essentially pretrained. Thus, each subnetwork can operate independently at inference time without retraining.

The team trained an OFA convolutional neural network (CNN) commonly used for image-processing tasks with versatile architectural configurations, including different numbers of layers and neurons, diverse filter sizes, and diverse input image resolutions. Given a specific platform, the system uses the OFA as the search space to find the best subnetwork based on the accuracy and latency tradeoffs that correlate to the platforms power and speed limits. For an IoT device, for instance, the system will find a smaller subnetwork. For smartphones, it will select larger subnetworks, but with different structures depending on individual battery lifetimes and computation resources. OFA decouples model training and architecture search, and spreads the one-time training cost across many inference hardware platforms and resource constraints.

This relies on a progressive shrinking algorithm that efficiently trains the OFA network to support all of the subnetworks simultaneously. It starts with training the full network with the maximum size, then progressively shrinks the sizes of the network to include smaller subnetworks. Smaller subnetworks are trained with the help of large subnetworks to grow together. In the end, all of the subnetworks with different sizes are supported, allowing fast specialization based on the platforms power and speed limits. It supports many hardware devices with zero training cost when adding a new device.In total, one OFA, the researchers found, can comprise more than 10 quintillion thats a 1 followed by 19 zeroes architectural settings, covering probably all platforms ever needed. But training the OFA and searching it ends up being far more efficient than spending hours training each neural network per platform. Moreover, OFA does not compromise accuracy or inference efficiency. Instead, it provides state-of-the-art ImageNet accuracy on mobile devices. And, compared with state-of-the-art industry-leading CNN models , the researchers say OFA provides 1.5-2.6 times speedup, with superior accuracy. Thats a breakthrough technology, Han says. If we want to run powerful AI on consumer devices, we have to figure out how to shrink AI down to size.

The model is really compact. I am very excited to see OFA can keep pushing the boundary of efficient deep learning on edge devices, says Chuang Gan, a researcher at the MIT-IBM Watson AI Lab and co-author of the paper.

If rapid progress in AI is to continue, we need to reduce its environmental impact, says John Cohn, an IBM fellow and member of the MIT-IBM Watson AI Lab. The upside of developing methods to make AI models smaller and more efficient is that the models may also perform better.

The rest is here:

Reducing the carbon footprint of artificial intelligence - MIT News

Airbnb wants to mold its hosts into a powerful organizing force, akin to a union, to advocate on its behalf with local governments around the world and to serve as an ideological rebuke to the advances of AI at other tech firms.

As part of that effort to increase engagement with hosts, CEO Brian Chesky announced today that he is embarking on a world tour, forming a host advisory board that will provide feedback to the company and sit in on one of its four annual board meetings, and do monthly check-ins with Airbnb users via Facebook Live.

Iwant to be held accountable to the community, Chesky, who is modifying his title to CEO and head of community, told a group of hosts gathered at Airbnb HQ. Its incredibly important because when we sit in a room trying to make decisions, we want to make sure were doing it for the community, not to the community.

Chesky will visit London, New York, Cape Town, Delhi, and Beijing to meet with hosts over the next couple ofweeks, and hinted that more changes are coming to improve customer service and host experience.

Putting hosts front and center is part of Airbnbs business strategy after all, the company relies on people to list their homes for rent but it also hints at Airbnbs transition into political advocacy. The company has been criticized by politicians for its impact on housing crises in major cities and by startup advisers for being caught flat-footed by regulators. But over the last two years, Airbnb has tried to shift those perceptions by taking a more active role in politics, most recently with its responses to the Trump administrationspolicies on immigration and transgender students.

Although those Trump-inspired efforts grab headlines, the bread and butter of Airbnbs political campaigning is still its hosts. Chesky linked the Trump effect and Airbnbs homegrown efforts to rising economic insecurity.

The president of the United States is an example of economic insecurity, he said. Im concernedabout the concept of automation. Many jobs will be automated; a lot will be. This will have benefits for people but it also has a huge cost. I worry that Made in America will become Made by robots in America.'

Cheskys remarks draw a line between the company he co-founded and other members of the so-called sharing economy like Uber, who see increased automation as the future of their businesses. Whereas Uber plans to replace its human drivers with self-driving vehicles, Chesky stressed that humans will remaincentral to Airbnbs business.Everything at Airbnb will have people at the center of it. Its an important commitment that everything we do will be powered not just by technology but by people.

Convincing customers to participate in political advocacy is also part of Ubers anti-regulation playbook, but Uber is betting longterm on riders, not drivers, as their best advocates. Airbnb thinks it needs both consumers and hosts for a successful advocacy equation.

Tech will never replace what you do, although it might replace other jobs, Chesky told the group of hosts. A robot can open a door and let you into a home, but its not going to make you feel welcome.

Highlighting the importance of hosts in Airbnbs ecosystem is part of the companys political strategy after Chesky and the hosts departed for a reception, head of global policy Chris Lehane briefed reporters on the ways Airbnb has successfully convinced its users to advocate for the company with local politicians. Lehane said that, since 2014, more than 10,000 Airbnb users have contacted an elected official and more than 50,000 have signed petitions to support the company. Drawing on his experience with political campaigns, Lehane noted that a down-ticket 2015 San Francisco ballot initiative to restrict short-term rentals drew more total votes than the mayoral race that year. Its an example of Airbnbs success in mobilizing its users as political advocates and Lehane said he raises the point with lawmakers, stressing Airbnbs popularity with their growing millennial constituencies.

Airbnb signs up new users through word-of-mouth, and Lehane said a similar approach has worked for political advocacy. What we know is that the majority of people who travel on Airbnb typically learn about it from friends or family, Lehane said. It translates into how our community thinks about people operating Airbnbs.Hosts advocate for usfrom a consumer perspective, but also advocate for us from a policy perspective.

To keep users politically engaged, Airbnb is expanding its host clubs from the 114 groups currently active around the world to 1,000 by the end of 2018. Lehane compared the clubs to unions or guilds and said they would serve as central hubs for organizing. They are more than political advocacy organizations, he said. Theyevolved froma policy tool to a broader movement built around our community.

See the original post here:

Airbnb pledges not to replace human community with AI - TechCrunch

When managers tour a site once or twice a week, the camera on their head captures video footage of the whole project and uploads it to image recognition software, which compares the status of many thousands of objects on sitesuch as electrical sockets and bathroom fittingswith a digital replica of the building.

The AI also uses the video feed to track where the camera is in the building to within a few centimeters so that it can identify the exact location of the objects in each frame. The systemcan track the status of around 150,000 objects several times a week, says Danon. For each object the AI can tell which of three or four states it is in, from not yet begun to fully installed.

Site inspections are slow and tedious, says Sophie Morris at Buildots, a civil engineer who used to work in construction before joining the company. The Buildots AI gets rid of many repetitive tasks and lets people focus on important decisions. That's the job people want to be doingnot having to go and check if the walls have been painted or if someones drilled too many holes in the ceiling, she says.

Another plus is the way the tech works in the background. It captures data without the need to walk the site with spreadsheets or schedules, says Glen Roberts, operations director at Wates. He says his firm is now planning to roll out the Buildots system at other sites.

Comparing the complete status of a project with its digital plan several times a week has also made a big difference during the covid-19 pandemic. When construction sites were shut down to all but the most essential on-site workers, managers on several Buildots projects were able to keep tabs on progress remotely.

But AI wont be replacing those essential workers anytime soon. Buildings are still built by people. At the end of the day, this is a very labor-driven industry, and that won't change, says Morris.

Change note: we have changed the text to clarify how the Buildots system differs from others.

Read the rest here:

AI that scans a construction site can spot when things are falling behind - MIT Technology Review