AI News, 65+ Statistics About Artificial Intelligence

Why AI is the Most Hyped Technology of Our Time

Accenture claims that Artificial Intelligence (AI) will double growth rates for 12 developed countries by 2035 and increase labor productivity by as much as a third.

Even if the AI industry only grabs 1/3 of the $15 trillion in economic gains, it still implies a market of about $5 trillion or almost 1000 times the market in 2018 or a growth of about 78% per year.

Although estimates for its global market in 2018 were between half ($4.5 billion) to a little larger ($12 billion) than AI’s current market ($9.6 billion), the size of its 2030 market is forecasted to reach $85 billion, or about 1% the size of the AI market.

These books were followed by a feeding frenzy of reports from consulting organizations each trying to paint optimistic futures for AI and other new information technologies such as blockchain, Internet of Things, and quantum computing in order to obtain clients.

This constitutes about 40 percent of the overall $9.5 trillion to $15.4 trillion annual impact that could potentially be enabled by all analytical techniques.” These are big numbers and they are a powerful incentive for organizations to pursue AI, either with or without help from McKinsey consultants.

For instance, its predictions of a 10% improvement in energy efficiency in the UK and elsewhere were based on the purported success of both DeepMind and Nest Labs, another subsidiary of Alphabet since 2014, which lost $621 million on revenues of $726 million in 2017.

And the advertised potential 2020 energy savings figure for the UK has been more than halved since the campaign began, dropping from £26 to just £11 a year for duel-fuel bills and the cost of smart meters and their installation has risen, not good news for the diffusion of smart meters.

My forthcoming article in IEEE Spectrum (Why Projections for AI’s Economic Benefits are Overly Optimistic) demonstrates that the most well-funded AI start-ups are not targeting productivity enhancing applications and many are likely incurring huge losses, despite hiring famous university researchers at hugely inflated salaries.

For instance, while five years ago experts expected Watson to become a common tool for doctors, or even replace them, news articles now trumpet studies that show AI can interpret medical images as well as humans, even though image recognition has been touted for many years as the best application for AI.

For instance, two years ago in 2017, McKinsey’s “review of more than 160 global use cases across a variety of industries found that only 12percent had progressed beyond the experimental stage.” And ones that have proceeded past the experimental stage were mostly in finance and telecommunication.

Although progress in error rates for image and speech recognition or performance at playing chess or Go have been reported,improvements in these algorithms occur more slowly than has Moore’s Law.For example, the word error rate fell three times between 2008 and 2014, the image error rate fell about four times between 2010 and 2015, and chess ratings for computers rose by 65% between 1990 and 2014 while Moore’s Law experienced doubling in the number of transistors every 18 months to 2 years over 50 years.

In support of Moore’s Law over these years, other algorithms have also experienced improvements over decades, impacting on various industries such as telecom and finance, but their impact has been much smaller than that of Moore’s Law.For example, new telecom standards have required new algorithms, but the implementation of these algorithms required the faster processing speeds that came from Moore’s Law.

For instance, Cathy O’Neil, once an implementer of big data, has been criticizing it for many years, and is the author of the 2016 book, Weapons of Math Destruction: Big Data Increases Inequality and Threatens Democracy.Her book details the problems that big data caused for hiring and scheduling workers, setting bail and sentencing criminals, targeting crimes, rising in a ranking, and targeting customers with ads.

In each case, big data does not address an underlying productivity problem and instead targets a superficial issue, often one concerned with capturing more value from workers or from customers than from improving productivity and it often encourages people to game the system.

Gary Smith, author of the 2019 book, the AI Delusion, addresses these types of issues in a more basic way, focusing on the fundamental challenges of data analysis, a highly manual process that requires better data than often exists.

He said: “You can see the computer age everywhere but in the productivity statistics.” The reason computers have not had the on productivity that many expected is because manual systems weren’t as inefficient as ordinarily thought and thus computers didn’t contribute large improvements in productivity outside of a few industries such as communications and entertainment.

The fact is that people, materials, and equipment are for the most part effectively organized in industries such as manufacturing, logistics, construction, retail, and wholesale and the last few decades of implementing IT has increased the effectiveness of their organization.One exception is personal transportation in which most personal vehicles sit for 95% of the time and the rest of the time sit in traffic.

British Consumers Are Not Ready for AI and Want Higher Levels of Personalisation, New Study Reveals

READING, United Kingdom--(BUSINESS WIRE)--UK consumers are not prepared for the introduction of Artificial Intelligence (AI), and want brands to focus on delivering a seamless, personal digital experience instead, according to the results of a global survey commissioned by Acquia, the open digital experience company.

Only two in five (44%) British consumers are looking forward to brands interacting with them via AI, such as chatbots and voice assistance technology, with four in five (81%) stating automated experiences with brands are too impersonal, in Acquia’s annual ‘Deliver the CX They Expect: Customer Experience Trends’ report.

Respondents to the online survey were 6,013 consumers – 1,000 from Australia, 1,002 from France, 1,001 from Germany, 1,006 from Mexico, 1,001 from the United Kingdom and 1,003 from the United States) and 600 marketers (100 from Australia, 100 from France, 100 from Germany, 100 from Mexico, 100 from the UK and 100 from the U.S.). The consumers were all 18 and older and the sample is balanced by age and gender for each country.

37 Customer Experience Statistics You Need to Know for 2020

Post summary: It wasn’t too long ago when every business claimed that the key to winning customers was in the quality of the product or service they deliver.

The Temkin Group found that companies that earn $1 billion annually can expect to earn, on average, an additional $700 million within 3 years of investing in customer experience.

For example, customers are willing to pay a price premium of up to 13% (and as high as 18%) for luxury and indulgence services, simply by receiving a great customer experience.

But the most convincing reason why CX has become so important is this: A Walker study found that by the end of 2020, customer experience will overtake price and product as the key brand differentiator.

If CX is to play an important part in your 2020 plans (and it should!), use this article to stay ahead of the top customer experience statistics in the upcoming year.

But, in order for your customers to like you, you need to get to know them, and then use this knowledge to deliver personalized experiences across the entire customer journey.

But gaining this in-depth knowledge about customers isn’t something that just happens. You need to collect customer data (i.e.Voice of Customer data) and bring out valuable insights from that data with speed and precision.

And how you feel after an interaction with a customer service center has a huge impact on your future purchase decisions.

A good interaction keeps you happy and satisfied, while a poor interaction could lead to you stop doing business with that company again.

According to Gartner’s research, companies that successfully implement customer experience projects begin by focusing on how they collect and analyze customer feedback.

IKEA invests heavily into customer experience.  This year alone, they’ve opened more stores, invested in its home delivery network and launched a brand new app –

In fact, in their 2020 report, PWC found that the number of companies investing in the omni-channel experience has jumped from 20% to more than 80%.

Adding to this, Adobe recently found that companies with the strongest omni-channel customer engagement strategies enjoy a 10% Y-O-Y growth, a 10% increase in average order value and a 25% increase in close rates.

By not providing a positive mobile experience, you’re putting business growth in jeopardy, as the graphic below shows.

However, for companies that aren’t, they’ve been slow to adapt to this trend – especially when it comes to customer support – as an overwhelming 90% of customers report having a poor experience when seeking customer support on mobile devices.

In their future of CX report, PwC surveyed 15,000 consumers and found that 1 in 3 customers will leave a brand they love after just one bad experience, while 92% would completely abandon a company after two or three negative interactions.

In 2020, companies should ensure that customers are able to find answers to their questions using a wide-range of self-service options.

With 90% of companies now planning to deploy AI within 3 years, this number is expected to grow to 40% by 2023.

With 9 out of 10 businesses competing mainly on customer experience, it’s the organizations that take customer experience seriously that will stand out from the noise and win loyal customers over.

This means creating complete customer profiles that help you understand and measure your customers’ behavior at every touch point, and across multiple channels.

The Top 5 Impacts of Artificial Intelligence (AI) in Logistics!

Blog post originally published by, and permission to publish here provided by, Adam Robinson on https://cerasis.com There’s no other way to describe it: Artificial Intelligence (AI) is revolutionizing the world of logistics.

By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.

Such insights are incredibly valuable in a sector like air freight, where it accounts for only 1 percent of global trade in terms of tonnage but 35 percent in terms of value.

Through advanced machine learning and natural language processing the system can understand the sentiment of online conversations and identify potential material shortages, access issues and supplier status.

Picking is one of the most labor-intensive parts of the logistic process, so Fizyr has crafted a solution which allows the robot to identify package-type – in less than less than 0.2 seconds – and physically move the item to the desired location.

The industry itself understands how big of a change big data will bring: according to Third Party Logistics Study, 81 percent of shippers and 86 percent of third-party logistics companies believe that using Big Data effectively will become “a core competency of their supply chain organizations.” Why?

Such figures cannot be easily improved at the source, so algorithms are being used to analyze historical data, identify issues and improve data quality to the level where significant transparency on the business is gained.

As written previously, these AI algorithms only require 5 to 10 percent of correct data in order to create a training dataset which can be used as a basis for data cleansing and enrichment.

According to logistics giant DHL, visual inspection powered by AI is identifying “damage, classifying the damage type, and determining the appropriate corrective action” faster than ever before.” IBM Watson is a prime example of what can be possible with AI vision.

Another good example is from retail giant Amazon, who utilize computer vision systems which can help to unload a trailer of inventory in only 30 minutes compared to hours without using such systems.

Charge ranges have been a problem in the past, but electric vehicles are quickly improving their distance capabilities with Tesla announcing last year that its Semi Truck will be able to drive as far as 800 kilometers on full batteries and can get an additional 600 kilometers range with just 30 minutes of charging.

The tech is having a holistic impact on the way we ship – and the forthcoming years and decades are sure to bring more collaboration between logistics companies and startups to deliver even more cutting-edge advancement.

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