AI is a genie out of the bottle, and it cannot be put back. In a world overwhelmed by data, AI is the way forward for organisations.

Computing has moved on from information organisation and manipulation. Digitisation is turning everything and everyone into data, and machines can convert data into vision, hearing, text, speech, movement, patterns and decisions. With cognition coming to machines, modern computing is about autonomous learning and action by machines. Artificial intelligence (AI) is here and is evolving fast. Businesses and managers must keep up. But outside the tech industry, AI is mostly a buzzword. Most organisations are yet to digitise comprehensively because of legacy issues. They lack the level of data that is required to drive business strategy with AI. Even the few that have flirted with AI technologies have done so in a piecemeal manner. While AI-armed innovators are disrupting industry after industry, conventional organisations continue to see AI as an IT function, and autonomous machines as means to saving costs.
AI really comes into its own when a business wants to blitz scale. Its primary promise is to enable extremely fast work. The way to appreciate what AI can do for a business is to look at e-commerce, digital advertising, taxi, ticketing and social media companies that have taken over the world by using AI to manage huge and complex systems.
App-based taxis are a useful model for managers to appreciate AI’s power in business. These have revolutionised urban travel by ensuring availability at customer’s location, providing credible time of arrival, setting a price by demand and deciding the fastest route—all in an instant. This model of matching, mapping and pricing is made possible by algorithms that continuously learn from new data about each ride. AI learns from patterns of time, place, weather, traffic to adjust fares, routes and availability for future rides. The lesson is that AI changes the game when every part of the business feeds live data and accepts direction from AI.
To use AI in business, management leaders have to be clear about what they want AI to do for them. Buzz-struck managers follow the herd without tailoring AI for the business strategy or syncing organisation competence with it. Many get conned into buying AI snake oil such as algorithms that claim to read body language to identify suitable recruits. To develop meaningful AI applications, management has to set clearly quantifiable deliverables that AI can be trained on. Having a comprehensive measurement system is a basic requirement for using AI. Moreover, an organisation must have the personnel who can curate, label and rank data for algorithms to make correlations and predictions.
AI is just a tool and not a strategy by itself. AI can help organisations define and implement the strategy much better by offering deeper understanding of everyday affairs and probabilities. It can help businesses reach more customers, customise offerings, increase efficiency, read trends, develop new capabilities and beat competitors on innovation.
Still, the real-time computing capability of AI can have a big influence on business strategy. Food retailers are now feeding data about purchases, weather, location, social media posts, etc, to algorithm to come up with best-selling menus and prices for different times, days and locations. Parking companies are also using data to anticipate demand and price the slots accordingly.
AI can build rigorous profiles of customers and underwrite trust that is necessary for e-commerce. Online retailers can offer free trials to customers they can trust by observing their shopping behaviour. A new innovation in online business is adding a social media functionality to the retail platform. This allows shoppers’ friends to approve or disapprove free trial items, which increases sales and reduces returns. The friends can be paid for commenting on each item. AI can also shape business strategy by providing clues to long-term value of each customer by predicting their loyalty and future purchases, and also the cost of keeping them. Such intelligence can shift focus from maximising transaction value to maximising relationship value.
While AI offers a big leap in business capability, it has its limitations and risks. The biggest limiting factor is data availability and its quality. A lot of people are not fine with surveillance, and some governments do not approve data collection and commercial use without permission. Some American tech giants have suffered severe brand damage and financial penalties for their data extraction practices and flawed AI applications.
Explainability of decisions is another big challenge for AI. With too many different inputs fed into AI calculations, it is a challenge to attribute decisions to specific criteria. Without explainability, AI can even produce wrong decisions. So, care has to be taken to get data selection, collection and feed right so that AI does not calculate with partial facts or blatant biases. For all its challenges, AI is a genie out of the bottle, and it cannot be put back. In a world overwhelmed by data, AI is the way forward and organisations have to hook their strategy and management with this revolutionary new technology of work.

SAN FRANCISCO — White House officials on Monday unveiled plans to increase federal funding for the development of artificial intelligence and quantum computing, two cutting-edge technologies that defense officials say will play a key role in national security.

The funding, part of the Trump administration’s $4.8 trillion budget proposal, would direct more money for A.I. research to the Defense Department and the National Science Foundation. The administration also wants to spend $25 million on what it calls a national “quantum internet,” a network of machines designed to make it much harder to intercept digital communication.

For several years, technologists have urged the Trump administration to back research on artificial intelligence — which could affect things as diverse as weapons and transportation — and quantum computing, a new way to build super-powerful computers. China’s government, in particular, has made building these machines a priority, and some national security experts worry that the United States is at risk of falling behind.

The proposed spending follows earlier administration moves. In 2018, President Trump signed a law that earmarked $1.2 billion for quantum research. The Energy Department recently began distributing its portion of that money — about $625 million — to research labs in industry, academia and government.

“The dollars we have put into quantum information science have increased by about fivefold over the last three years,” said Paul Dabbar, under secretary for science at the Energy Department, in an interview.


Last year, Mr. Trump signed an executive order that made A.I. research and development a national priority. 

The new budget proposal would increase funding for artificial intelligence research at the Defense Advanced Research Projects Agency, a research arm of the Defense Department, to $249 million from $50 million, and at the National Science Foundation to $850 million from about $500 million. The administration also vowed to double funding for A.I. and quantum computing research outside the Defense Department by 2022.

Big tech companies have invested heavily in A.I. research over the last decade. But many experts have worried that universities and government labs have lost much of their talent to businesses. Under the new funding plan, the National Science Foundation would apply $50 million to help train A.I. experts.

The world’s biggest technology companies, from Google in the United States to Alibaba in China, are also racing to build a quantum computer, a new kind of machine that could be used to break the encryption that protects digital information. Researchers are using the same scientific principles to create new technology that could withstand such an attack.

In 2017, after four years of planning and construction, China unveiled a dedicated quantum communication network between Beijing and Shanghai. Two Chinese provinces invested $80 million in the project. It has also tested quantum encryption techniques via satellite.

With the $25 million, the Energy Department would build a network connecting its 17 national research labs, which include Los Alamos in New Mexico and Argonne outside Chicago. Using this test network, researchers would explore quantum encryption technologies with an eye toward creating a secure network across the country.

“This is a test bed for new technologies,” said David Awschalom, a professor at the University of Chicago who oversees much of the university’s quantum research and would play a role in the effort at the national labs. “We are using the power of the national labs to fuel the country.”

Source: NY Times

The AI industry has been growing rapidly over the past few years, to such an extent that some now class it as an industry which has gone from “emerging” to one that has fully “emerged.” As we enter 2020, it seems like the perfect opportunity to assess the industry as a whole.

It is easy to forget just how quickly technology has advanced over the last 10 years. For instance, we take fingerprint and facial recognition for granted now as we do with so many other features when using our devices every day. However, at the turn of the last decade, social media was still in its infancy and many products, such as Amazon’s Alexa, were still years away from being released. We are now at a stage where AI has filtered through to consumer products that are readily available to buy, such as the aforementioned Alexa and Google Home devices. According to a report by EY, around a quarter of British homes have a smart home assistant device, demonstrating the normalization of human and technological interaction. Furthermore, in the next five years, 41 percent of respondents said that they plan to own one.


Whilst these figures demonstrate the growth of the AI industry, people still have reservations about AI, mostly related to safety and security. Advancements in machine learning during this time means that AI is now smarter than ever and is getting smarter all the time, as a result, 71 percent of households are worried about people potentially hacking into their smart home devices and accessing their personal and private data. A big part of this scepticism is fear of the unknown with regards to the potential capabilities of AI devices, which has been enhanced in popular culture with certain devices turning “evil,” such as in 2001: A Space Odyssey’s HAL and Black Mirror’s “Metalhead” episode in which robotic dogs were programmed to destroy everything in their path.

Whilst such inventions are still years away and remain the objects of our imagination, this has brought a reticence to change which is a natural human instinct. This has been furthered by experts such as Yuri Anderson, entrepreneur in residence at QTEC (City AM), who said, “The IBM Watson and DeepMind have already shown that machines can, in at least some ways, outsmart humans,” creating concern that scenarios like Hal and Metalhead can occur in the future.

However, these are all worst-case, extreme scenarios, and lots of influential people in disruptive industries are investing millions in AI technology. CEO of Tesla Elon Musk warned about the potential dangers of AI as he believes it will become humankind’s biggest existential threat, yet his AI project to replicate the human brain received a $1 billion boost from Microsoft in 2019, suggesting he may have backtracked on his initial suggestion. Tech and real estate entrepreneur Tej Kohli has also invested millions into AI with his Rewired venture studio, which offers resources to various projects concerned with AI and machine learning. The reason he and other investors are so keen to invest in the industry is because of the benefits it can have, such as in the services industry with personal assistants and chatbots. The more developed the machines become, the better they will be placed to understand what humans want.

The growth of the AI industry has become much more influential in society over the past few years, and one wonders where the next stage of growth will take it, and how beneficial it will be to society.

Source:Grit Daily


Tech giant IBM cemented its patent leadership in the United States, with over 9,000 received patents, including in artificial intelligence (AI) and blockchain, in 2019.

With the 9,626 patents secured, IBM became the company that received the most patents in the U.S.ever and the leader on the list for the 27th consecutive year, tech-focused media outlet Database Trends and Applications reported on Feb. 4.

Of the 9,626 patents, IBM secured over 1,800 patents in AI, more than 2,500 patents in cloud technology and led in blockchain patents received.

IBM’s patent dominance

Back in July 2019, Cointelegraph reported that IBM had tripled the number of blockchain patents secured in the U.S. since 2018, boasting over 100 active patent families at the time. That made IBM’s growth in U.S. patents the largest of 2018. 

At the time, Yuval Halevi, co-founder of crypto and blockchain PR company GuerillaBuzz, commented: “In just 1 year the number of IBM blockchain patents has grown by 300%. When one of the largest companies in the world (366,000 employees) spends so much of their resources on developing a blockchain department, this tells a lot about the market potential.”

Just recently, IBM was awarded a patent for the development of a “self-aware token” designed to track and record events of offline transactions. Last year, IBM added to its stack of blockchain patents an application that aims to improve the security of permissioned blockchain networks and an application that focuses on database management using blockchain, among others.

Blockchain patent battle continues

The news comes amid the so-called battle for the blockchain dominance between the U.S. and China. As an analysis for Cointelegraph published in late November 2019 showed, China was already winning the Intellectual Property arms race against the U.S. 

The authors of the analysis examined blockchain-related patents that had been granted from January 2014 to October 2019 by China’s patent office, the National Intellectual Property Administration, or CNIPA, and the U.S. Patent and Trademark Office, or USPTO. During that period, the CNIPA awarded 2,218 blockchain patents compared to 227 by the USPTO.

Meanwhile, the NIPA in China was set to issue clarified guidelines for blockchain patent applications on Feb. 1.

Source: cointelegraph

Last week, Microsoft announced the latest news in its ongoing “AI for Good” program: a $40M effort to apply data science and AI to difficult and comparatively under-studied conditions like tuberculosis, SIDS and leprosy. How does one responsibly parachute into such complex ecosystems as a tech provider, and what is the process for vetting recipients of the company’s funds and services?

Tasked with administrating this philanthropic endeavor is John Kahan, chief data analytics officer and AI lead in the AI for Good program. I spoke with him shortly after the announcement to better understand his and Microsoft’s  approach to entering areas where they have never tread as a company and where the opportunity lies for both them and their new partners.

Kahan, a Microsoft veteran of many years, is helping to define the program as it develops, he explained at the start of our interview.

John Kahan: About a year ago, they announced my role in conjunction with expanding AI for Good from being really a grants-oriented program, where we gave money away, to a program where we use data science to help literally infuse AI and data to drive change around the world. It is 100% philanthropic — we don’t do anything that’s commercial-related.


Here is the article details

When the topics of Microsoft  and global health overlap, one tends to think about the Gates Foundation, but the company itself is doing good work along these lines as well. The latest such effort is AI for Health, a $40 million, five-year outgrowth of Microsoft’s AI for Good program that aims to help apply the benefits of AI with an eye to bettering the health of the less fortunate worldwide.

The new initiative will focus on direct research in the medical AI field (think algorithms for automatically detecting a disease), global health studies (that is, better understanding of how such things could be of use) and improving access (actually putting the algorithms to work).

“AI for Health is a philanthropic initiative that complements our broader work in Microsoft Healthcare,” wrote Microsoft’s John Kahan in a blog post announcing the new program. “We will support specific nonprofits and academic collaboration with Microsoft’s leading data scientists, access to best-in-class AI tools and cloud computing, and select cash grants.”

Kahan points out that modern healthcare is incredibly unevenly distributed, coming near eliminating some diseases and forms of death in some countries, while others are ravaged by the same. That’s not exactly a problem that AI can solve, but there are things that it can do.


For instance, he points out, there are highly effective AI-based screening systems for diabetic retinopathy, a condition millions are at risk of that can lead to blindness. Getting a village access to a mobile phone and eye-inspection attachment is a lot easier and cheaper than dispatching an ophthalmologist.

It’s the goal of AI for Health to help engineer, identify and deploy technologies like that. Part of that is simply a question of cost — many AI experts are in the more general tech sector because that’s where the jobs are. Getting them to cross over to the social-good side means those projects will need to be competitive and successful, which a bit of Microsoft cash might help with.

The company noted a few partnerships that will benefit from the new program, with medical research outfits looking into SIDS, leprosy, diabetic retinopathy as mentioned above, tuberculosis, maternal mortality and, of course that eternal adversary, cancer.

Unfortunately, unlike some of Microsoft’s other grant programs, this $40 million isn’t up for grabs via public applications: It will be working directly with nonprofits and research organizations. But if you’re at one of those organizations, it might be a good time to get in touch with your collaborators in Redmond.

Source: TechCrunch


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