Category: Digital Economy

18 Jul 2019
Intel Developing an AI Chip That Acts Like a Human Brain

Intel Developing an AI Chip That Acts Like a Human Brain

Intel is aiming to develop semiconductors that mimic the way human brains work, announcing a new product dubbed Pohoiki Beach.

The neuromorphic chip processes data similar to how a human brain does, overcoming the challenges plaguing the first generation of artificial intelligence chips. With this product, Intel is extending AI into the areas that work similar to human cognition including interpretation and autonomous adaptation.

Intel’s Betting Next-Generation Chip Will Take AI to New Level
“This is critical to overcoming the so-called ‘brittleness’ of AI solutions based on neural network training and inference, which depend on literal, deterministic views of events that lack context and commonsense understanding,” Intel wrote in a research report. “Next-generation AI must be able to address novel situations and abstraction to automate ordinary human activities.”

Intel pointed to self-driving vehicles as one example where this new AI chip would be necessary. As it stands, the semiconductors used in autonomous cars can navigate along a GPS route and control the speed of the vehicle. The AI chips enable the vehicle to recognize and respond to their surroundings and avoid crashes with say a pedestrian.

But in order to advance self-driving cars, the systems need to add the experiences that humans gain when driving such as how to deal with an aggressive driver or stop when a ball flies out into the street. “The decision making in such scenarios depends on the perception and understanding of the environment to predict future events in order to decide on the correct course of action. The perception and understanding tasks need to be aware of the uncertainty inherent in such tasks,” researchers at Intel wrote.

New Chip Approach Will Speed Up Processing Times for AI Workloads

According to the Santa Clara, California semiconductor marker, with this new approach to computer processing, its new chips can work as much as 1,000 times faster and 10,000 times more efficiently when compared to the current central processing units or CPUs for artificial intelligence workloads. The Pohoiki Beach chip is made up of 64 smaller chips known as Loihi which when combined can act as if it is 8.3 million neurons, which according to one report is the same as the brain of small rodent. A human brain has nearly 100 billion neurons. 

Intel said the new chip can be particularly useful in the processing for image recognition, autonomous vehicles, and robots that are automated. The chip is free for developers focused on neuromorphic, including its more than sixty partners in the community. The aim is to commercialize the technology down the road. 


15 Jul 2019

Artificial intelligence (AI) in Construction Market to Hit Value of USD 3,161 Million By 2024

According to the report, the global AI-in-construction market was valued at USD 312 million in 2017 and is expected to reach USD 3,161 million by 2024, growing at a CAGR of 38.14% between 2018 and 2024.

New York, NY, July 14, 2019 (GLOBE NEWSWIRE) — Zion Market Research has published a new report titled “AI-In-Construction Market by Technology (Natural Language Processing and Machine Learning and Deep Learning), by Component (Solutions and Services), by Deployment (On-Premises and Cloud), and by Application (Project Management, Risk Management, Field Management, Supply Chain Management, and Schedule Management): Global Industry Perspective, Comprehensive Analysis, and Forecast, 2017—2024”.

According to the report, the global AI-in-construction market was valued at USD 312 million in 2017 and is expected to reach USD 3,161 million by 2024, growing at a CAGR of 38.14% between 2018 and 2024.

Artificial Intelligence allows computer systems to make intelligent decisions by applying the required skills. Artificial Intelligence has been beneficial in the development of applications that comprise machine vision for easy analysis and surveying of buildings and structures. Additionally, the development of creating information modeling is software that gives information on a construction project, warranty details regarding material used, and commissioning data. This has resulted in increased AI adoption by most of the construction start-ups globally for various applications.

Browse through 56 Tables & 29 Figures spread over 145 Pages and in-depth TOC on “Global AI-In-Construction Market: By Technology, Size, Share, Types, Trends, Industry Analysis and Forecast 2017—2024”.

Artificial Intelligence has the ability to perform tasks similar to that performed by human intelligence, such as planning, recognition, and decision making. The construction sector is adopting AI to obtain precise data and insights to increase productivity, operational efficiency, and ensure safety at work. AI operates on algorithms related to image recognition to find out search criteria. For instance, it includes hard hats and safety vests to search construction workers, those who are not wearing proper safety gears. The primary applications for AI-In-Construction market include planning, safety, monitoring and maintenance, and autonomous equipment.

AI’s capability in construction services and solutions to reduce production costs is the major factor expected to drive the global AI-In-Construction market. In addition, the need for safety measures on construction sites is also projected to drive this market’s growth. Furthermore, huge investments made by construction companies from the emerging economies globally in the adoption of the advanced AI technology for construction applications is also likely to contribute toward the global growth of the AI-In-Construction market. However, the low technological investments in R&D for developing new technologies might hamper this market. Nonetheless, the increasing demand for integrated AI in construction activities is estimated to create new market opportunities.

By technology, the AI-In-Construction market is divided into natural language processing and machine learning and deep learning. By component, this market includes solutions and services. By deployment type, the market is bifurcated into on-premises and cloud. A cloud deployment type is estimated to grow at a higher CAGR during the projected period, owing to its cost-effectiveness. Project management, field management, risk management, supply chain management, and schedule management comprise the application segment of the AI-In-Construction market.

North America dominated the global AI-In-Construction market in 2017, due to the lack of a skilled workforce that has driven the key construction enterprises to invest in robotics-based solutions. The real estate organizations are developing solutions that can detect the risks and perform the labor tasks repetitively, which can enable the non-experienced staff to complete the complex tasks. In addition, the high AI demand for various applications, such as field management, project management, and risk management, is likely to contribute toward this regional market’s growth.

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13 Jul 2019
Artificial Intelligence and Machine Learning Empowering Business Growth for Entrepreneurs Today

Artificial Intelligence and Machine Learning Empowering Business Growth for Entrepreneurs Today

In 2018 alone, AI-related startups and ventures have secured $9.3 billion in VC funding, according to a report from PwC and CB Insights. The Indian unicorns PayTm, Swiggy, and Oyo have been investing resources to gain AI capabilities and have acquired at least one AI company.

In 2018, VCs have funded Indian AI startups with USD 478.38 million in 111 funding rounds. One of the reasons why AI & machine learning-based technologies on the rise are that the competitive advantages one can develop using them, especially with the customer experience and cost optimization. In e-commerce, for example, understanding consumer behaviour and product demand and making the right offer at the right time can be the difference between winning or losing over the competition. 

Change in Working

AI in the early stages was mostly based on rule-based systems, whose ability to deliver value is limited by how well the rules are defined, which requires human expertise. In machine learning (ML) — a subset of AI — once a model is trained, the ML model can learn further through inferencing — where the ML model is put to work on real-time data. 

The ability of ML models to self-learn with minimal to no human interventions has been the key in gaining interest from entrepreneurs and innovators. Whether it’s shopping on Flipkart or watching movies on Netflix, the customers’ experiences are vastly touched my machine learning and its subset deep learning. Curating vast amount of content and making purchase recommendations has been one of the commercially well-recognized use cases that saw huge interest from tech giants as well as well-established startups. UBS estimates that AI as a standalone industry has the potential to reach a market cap of USD 120-180 billion by 2020. 

Health Care and AI

Healthcare is another field where AI and machine learning systems are expected to make a big impact. For example, a Bangalore based startup delivers precision medicine using AI and machine learning. Another Bangalore based startup analyzes medical data and generates reports using deep learning systems, which can make a huge difference in delivering timely patient-care. 

Other Sectors

Machine learning is the turnkey technology that impacts many industry sectors: robotics, retail, banking, finance, self-driving, fraud-detection, weather forecasting, finding medicine for HIV, examining extraterrestrial objects, and so on. That is, AI & ML provide massive opportunities for entrepreneurs to explore, experiment, and build new businesses. 

AI and machine learning applications need the power of massively parallel processing capabilities provided by GPUs. However, owning GPU hardware doesn’t justify 3-year amortization costs, especially for entrepreneurs who are starting out in the AI & ML space. Also, there aren’t many cost-effective GPU solutions available in the Indian market. Getting started with machine learning has become less difficult with the development and production-grade availability of Open Source frameworks.

In Conclusion

AI & ML space is still young and entrepreneurs have a massive opportunity to innovate and disrupt. There have been concerns that AI might replace our jobs on a massive scale. However, according to a study by UBS, in most areas, AI is poised to replace tasks, not jobs. 


11 Jul 2019
Artificial Intelligence And The Challenge Of Global Governance

Artificial Intelligence And The Challenge Of Global Governance

Digitalization is evolving from an economic challenge to a governance and political problem. Some studies suggest that by 2030, Artificial Intelligence (AI) might contribute up to EUR 13.33 trillion to the global economy (more than the current output of China and India combined). The essence of the political conflict that raises the issue of global governance is what type of actor (a state or a digital corporation) will lead this process, creating global asymmetry in terms of trade, information flows, social structures and political power. This means challenging the international system as we know it.

AI is generating new large-scale systems based on (1) services (such as traffic management and smart vehicles, international banking systems, and new healthcare ecosystems); (2) global value chains, the Internet of things (IoT) and robotics (Industry 4.0); and (3) electronics with a new generation of microprocessors and highly specialized chips. The “food” for AI is the Internet – a major source of data, computing power and telecommunications infrastructure.

Not all countries will benefit in the same way, since AI-driven wealth will be dependent on each country’s readiness to be “connected” to the Internet. This is the essence of the political problem between a non-territorial space based on large-scale computer networks and nation-states.

Both democratic and non-democratic governments struggle to assert authority over different dimensions. First, they need to regulate de facto global private monopolies (such as Google, Facebook, Apple and Amazon) that are setting new rules of competition, creating new technology markets and blurring boundaries between industries. Second, many countries perceive Internet governance as too US-centric. A good example is the governance of IP addresses, which created the precedent of having a “universal resource” managed by a private, US-based institution like the ICANN. Third, a vast majority of digital innovations and AI technologies and applications come from a unique public-private ecosystem in the United States: Silicon Valley. Finally, although the Internet is global, investment in infrastructure (such as 5G) requires huge investments driven by local (ex-public) telecom operators whose business models are less sustainable.

While the European Union struggles to regulate Silicon Valley’s global platforms, China has started to block them with a “digital wall,” promoting protectionism to strike and compete in the global-tech game. Examples are tech giants such as Tencent, Baidu, Alibaba and the impressive Digital Silk Road to connect the European Union and China with various types of infrastructure, including satellites, 5G and submarine cables. This project could be the infrastructure that will enable China, by 2030, to become “the world’s primary artificial intelligence innovation center, transforming the country into a leading innovation-style nation and the greatest economic power,” as China’s national AI plan states.


06 Jul 2019
AI in government: What should it look like and how do we get there?

AI in government: What should it look like and how do we get there?

With today’s widespread focus on artificial intelligence, it is hard to imagine that this is not the first time AI has held a prominent place in the zeitgeist. Back in the mid-1980s, during the first computer revolution, AI was gaining ground as a significant field of research that could revolutionize the world. Then it stalled and the so-called “AI winter” began. The ideas were right, but computing technology was just not there.

The 1980s might not seem that long ago to those of us who were in high school or college then (wasn’t that just yesterday?), but in technological terms, it was an epoch ago. Fast forward to today. AI has come out of hibernation with a vengeance, propelled by enormous advances in computing capacity, and with many promises to society.

In few places is AI ready to make as profound an impact than in the missions of federal agencies. Yet while AI is gaining traction in the minds of federal managers, agencies still face several challenges — both technical and in mindset — that must be overcome first.

Federal momentum

If 2018 was the year AI entered the federal collective consciousness, 2019 is the year government starts seriously considering where and how it can help. In February 2019, President Donald Trump signed an executive order calling on the government to invest in, support and accelerate the use of AI initiatives in federal applications.

A month later, fiscal 2020 budget proposals released from the White House showed the federal government was preparing to allocate some $4.9 billion into unclassified AI and machine learning research. A week after that, the administration launched to be “the hub of all the AI projects being done across the agencies.” The federal AI/ML agenda is beginning to coalesce, but it is still without defined form.

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Defining artificial intelligence
One of the challenges that came with the rapid re-entrance of AI into the federal mind is a muddying and confusion of the terms. The term “AI” is now being used to describe solutions on a long continuum of automation capabilities. Indeed, it is easy to see why: the Defense Advanced Research Projects Agency’s (DARPA) use and development of AI should be very different from another agency’s use of AI on a continuum of machine learning, such as the Agriculture Department’s National Institute of Food and Agriculture. Nevertheless, the first step agencies should take is to clearly identify how they define AI and what types of AI are appropriate for their various mission objectives.

For many government missions, the most useful AI isn’t what researchers call artificial general intelligence — that is AI that will be designed to think and reason for itself. Instead, the government should look for solutions that leverage AI intended to make human decisions in a singular high-volume but low-involvement function. In other words, the mundane tasks that, if automated, would free up resources for other more challenging or creative jobs.

A textbook example of this is in IT operations and cybersecurity, where AI is able to look at machine data from the world around them — log files from IT systems, Internet of Things (IoT) data, user behavior activity, etc. — and then use that to derive insights and automate decisions that are going to provide the most value.

Leveraged against the growing trend of improving citizen experience and engagement, such AI might be deployed to automate certain approvals, validate claims, accelerate permit processing or more. In the near future, I’d expect to see AI participating in everything from education loans to tax returns, drastically increasing the ability for the government to react to citizen demands, reduce inefficiencies and more. The applications of AI are far-reaching and widely open but first comes a little homework.

Find your dark data
As tempting as it is to deploy a solution that includes AI in its feature set and call it a day, deriving real value from AI is going to rely on quality data to train and support it. However, that is one area still challenging agencies — and the private sector as well.

A recent survey showed that public sector technologists and leaders estimated 56% of their data was still “dark” or “grey,” meaning it was unknown or unusable within the organization. In other words, agencies are missing more than half the picture of their operations. Magnified by the force multiplier that is data insights and AI, and it is hard to put a number on the value agencies are missing.

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Getting a better hold on this dark data is a crucial first step agencies must take before adopting AI. Which brings me to my next point…

Don’t over-design to specific use cases
It is tempting, particularly in government, to follow this approach: Understand what data you have, think through the use cases and how it can be deployed, then solicit requests for proposals (RFPs) for a broad solution to achieve that mission. This approach has served government well for decades, but is simply not agile enough for the anticipated developments in machine learning and applications of AI, especially with the associated complexity and dynamism of data.

Data is growing too fast and becoming too complex to build and rely on a rigid data architecture designed to ingest, analyze, process and support specific data for a mission. This approach limits agility, flexibility and creativity as it forces data to live in formats contrary to its nature.

A more robust approach would be to understand what data you have access to, collect as much of this data as is feasible and then consider what uses there might be for it. Chances are collecting data you didn’t know you had or needed will reveal new uses for that data, new insights into the mission and ultimately better mission success. Indeed, numerous organizations that have taken this approach have proven that the effort will not be wasted.

For example, the National Ignition Facility at Lawrence Livermore National Laboratory leverages this approach to technology to enable a smooth user experience for world-renowned scientists and engineers, conducting experiments that ensure the country’s continued competitive advantage in scientific research. While collecting vast amounts of data from a wide range of sensors, including cameras, thermometers and motors, NIF applies algorithms in real time to identify anomalies before they become problems. As a result, NIF engineers can detect when these sensors begin to decay and perform predictive maintenance, avoiding unscheduled downtime for groundbreaking scientific experiments. A machine learning toolkit comes in handy when you don’t know the value of your data yet!

Just as federal agencies were driven to the cloud a decade ago but were unsure of how to proceed and how to leverage it, so too, agencies are being driven to artificial intelligence: uncertain of how to proceed and how to leverage it.

But as with cloud adoption, by all accounts a mainstay of government technology policy today, the best way to begin your data-driven journey and path toward AI is to dig into the data and start using and experimenting with it. Collect more than you need and do not worry about if it will be useful up front. The uses will make themselves clear and the value will come.


01 Jul 2019
How AI Is Helping Keep You Safe Online and In the Real World

How AI Is Helping Keep You Safe Online and In the Real World

AI is a vital component in keeping you safe from cyberattacks online and criminals in the real world.

Artificial Intelligence, or AI, is something of a buzzword of late, but it is a very powerful tool in our digital age. One of its most important roles is security – both in our digital and real-world lives.

Here we will briefly explore where it is being used, how it is being used, and provide some current interesting examples.

How is AI being used in security?
AI and machine learning are being employed increasingly around the world to help preserve and improve security in many ways. From helping keep ahead of the fast-paced development of cybersecurity threats to helping law enforcement and security services prevent criminal acts, AI is becoming an essential tool to keep us all safe from malicious, or even dangerous, neer-do-wells.

Here AI can be employed to help, often, under-resourced security operations analysts stay ahead of the curve. AI can, for example, curate all current knowledge of threat intelligence to help provide threat insights almost instantaneously.

This helps reduce response times to cyberattacks considerably. AI can also be trained to learn by consuming billions of data artifacts from structured and unstructured sources.

This could include blogs and news stories and allows the AI, using machine learning, to improve its knowledge of cybersecurity over time. More sophisticated ones, like IBM’s Watson, can even employ a form of cyber-reasoning to find relationships between suspicious files or IPs in seconds to minutes.

This greatly improves cybersecurity analysts response times to potential threats.

What companies are working on artificial intelligence security?
There are various companies working on AI security. Many larger organizations, like Microsoft and IBM, also have AI-security departments.

IBM’s Watson and Microsoft’s Windows Defender are examples of AI-based security solutions.

As we previously mentioned, AI can also help with physical security. Companies like Liberty Defense, a concealed weapon detection company, have been working on an AI-powered solution to help reduce weapon-related crimes.

Called HEXWARE, the weapons detection system uses active 3D imaging and AI to assess and detect threats as groups of people pass its sensors. It was developed at MIT’s Lincoln Laboratory can have been designed to be installed at the perimeter of any building.

It can be used both indoors and outdoors and detect both metallic and non-metallic threats. This is one of the few technologies of its kind that doesn’t also use facial recognition – which protects the privacy of individuals.

The idea is to allow a site’s security to identify a possible threat before it even enters the building.


30 Jun 2019
Sensors and metrology as the driving force for digitalization

Sensors and metrology as the driving force for digitalization

Many digitalized processes depend on data collected by increasingly powerful sensors and other test and measurement technology. When this data is processed, it provides precise and reliable information about the operating environment. Nine Fraunhofer Institutes will be presenting the results of their research into sensor technology and its applications in the field of testing and measurement at Sensor+Test 2019 in Nürnberg from June 25 to 27 (Booth 248 in Hall 5).

A great many innovations in today’s digital era rely on the ability to transfer information from the real world to the digital universe—examples include advances in gesture recognition, non-contact materials testing and artificial respiration. In applications like these, sensors and other test and measurement systems can be equated to enabling technologies because many new developments are based on them. At this year’s edition of Sensor+Test, the leading forum in this field worldwide, Fraunhofer will once again be presenting examples of its research in the many areas that make up its wide-ranging technology portfolio.

Wider-spectrum contact-free materials testing

Terahertz imaging is one of the new technologies that is being used increasingly to monitor  and test new materials. This non-contact method can be used to measure coating thickness, analyze the structure of polymer composites, or detect defects in non-conductive materials. The Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, HHI, will be presenting the next generation of fiber-coupled terahertz transceivers. The integrated sensor probe permits reflection measurements orthogonal to the surface of the test sample and can be used without modification in combination with commercially available terahertz measuring systems.

Reducing machine downtime, manufacturing defects and reject rates

The Fraunhofer Institute for Digital Media Technology IDMT will demonstrate how the quality of workpieces and components can be assured using a non-contact, non-destructive test method based on audio sensing of product and process parameters combined with machine learning. Visitors can learn more about this method, which can be used both to monitor production processes and to perform end-of-line product testing, in a series of interactive exhibits.

Supplying sensors with energy created by tiny vibrations

One of the challenges in the Internet of Things (IoT) is how to supply power to wireless sensors—a question that Fraunhofer Institute for Integrated Circuits IIS is tackling by developing energy harvesting solutions. Even the slightest vibrations generating a pressure of 100 mg at a frequency of 60 hertz are sufficient for a vibration transformer to produce the electrical energy needed to operate several sensors and transmit data once per second. The Maximum Power Point Tracker provides an effective means of controlling the charge converter so as to guarantee a maximum power yield. The energy harvesting solution recharges the battery while the device is in operation and enables the design of IoT sensors with an unlimited service life, without power cable or swapping batteries.

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29 Jun 2019
How AI and human-machine collaboration is driving transformation across sectors

How AI and human-machine collaboration is driving transformation across sectors

Even a decade ago, the mention of Artificial Intelligence (AI) might refer to the fear that it would take away human jobs and render them expendable. Cut to the present and that fear has now been replaced with a more rational approach where AI is being seen as a way to extend human capabilities in an increasingly digital era.

The potential of AI to transform the way business is done and benefit individuals, communities and society as a whole is amply evident across a number of spheres – from recommendation engines that note customer preferences and suggest relevant items that they may want to buy, to chatbots that can enhance the customer experience to making healthcare and diagnostics more accurate and affordable, or even ensuring public safety.

When humans and machines collaborate, there is a lot more that can be achieved than when there is singular effort, and the following are just a few instances of how AI is helping redefine every aspect of our life.

Making everyday life easier and more efficient

When shopping online, you cannot miss those suggestions to buy items that pop up on your screen which seem to have an uncanny insight into your mind. Today, recommendation engines driven by AI and ML are a significant part of e-commerce, and play a huge role in the customer experience journey.

In the financial sector, AI is used across a variety of functions, from detecting and handling frauds, to assessing risks, and in advisory services, all of which work to ensure that your money is safe.

When we hail a cab or use the map feature on our smartphone to navigate, it is AI which is getting us to our destination. It’s the same when we ask our smart device to stream our favourite song.

In shorttoday, AI plays a big role across all aspects of everyday human lives: from how we shop, how we bank, commute or unwind.

Exploring a new frontier in healthcare

AI is completely changing how we look at and deal with health-related issues and patient outcomes. It brings into play more meaningful insights and more intelligent processes with a focus on reducing manual work, providing more accurate services and impactful interventions to patients, as well as long term savings for everyone involved.

From robotic surgeries for accurate and precise operations, to electronic health records easily accessible by all stakeholders, virtual health assistants which stay ahead in managing patients’ health, and accurate diagnostics, the emergence of use cases for AI in healthcare are on the rise.

Enhancing customer service through bots

Today’s customers expect an “always-on, always-me” experience. Here is where conversational bots, i.e.AI-powered messaging solutions, are saving the day. Users can interact with such bots, using voice or text, to access information, complete tasks or execute transactions. 

In a survey by Accenture, 56% Of CIOs and CTOs surveyed said that conversational bots are driving disruption in their industry, while 57% agreed that conversational bots can deliver large ROI for minimal effort.

These bots are capable of performing complex tasks by combining one or more interfaces. With advancements in technology, in the future, bots will be able to act without human intervention and take relevant actions.

Despite the scepticism around bots on whether they will be able to appropriately incorporate history and context to create the personalised experiences desired by a customer and adequately understand what he or she requires via human input, businesses are embracing bots. Today, these virtual agents help enhance human agents’ productivity, deliver timely, conversational and contextual customer interactions and help resolve issues in a speedy and satisfactory manner.

Explainable AI to serve the ‘missing middle’ space in human-machine interaction

The rapid adoption of AI and related technologies notwithstanding, there will always be some jobs that will be done exclusively by humans. And then there are others which can be fully automated and taken care of by intelligent automation. But the maximum roles will see a combination of humans and machines working together. This space is something termed as “the missing middle” by Accenture

There are situations where an AI-driven decision on its own is not enough and we also need to know the reasons and rationale behind it. These roles will require people to apply their human skills and intelligence. Explainable AI complements and supports humans enabling them to make better, more accurate and faster decisions.

As collaboration between humans and machines increases, this space will see more action. For example, large enterprises have to manage a huge number of projects which means interacting with multiple vendors, clients and partners. The risks involved for each of these interactions is different, and often companies go wrong because of the complex nature of these interactions. Accenture Labs applied Explainable AI and developed a five-stage process to explain the risk tier of projects and contracts at each tier, along with valid reasons for these predictions, making it easier for decision makers to take more informed decisions.

Accenture Ventures’ Applied Intelligence Challenge

We have seen how AI’s footprint extends across industries, sectors and use cases, and helps make things better, faster and more efficient. Now let’s talk something even cooler: Applied Intelligence. Accenture’s unique approach to combining AI with data, analytics and automation helps transform businesses — not in silos, but more comprehensively across all functions and processes, helping them maximise existing investments, extending new technologies and scaling opportunities as they arise.

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27 Jun 2019
The Only Thing We Have To Fear Is The Fear Of AI

The Only Thing We Have To Fear Is The Fear Of AI

It is now the era of artificial intelligence; the age of the synthetic mind. An increasing number of the world’s leaders all acknowledge this increasingly evident truth. The question now is not whether AI is coming (it is here), or how large an impact it will have (it could automate 35% of our jobs by the 2030s and is at the center of the burgeoning “Tech War” between China and the United States). It is instead how society will choose to engage with this new revolution. Will we configure our public education, social contracts, business models and international agreements to capitalize on the incredible new potential AI brings, or will we shrink back in fear?

Certainly, there are developments that should be concerning. The open and transparent development of AI is at risk as looming export controls threaten to pull it into an ever expanding “Tech War” between China and the United States. Autonomous weapons are probably being developed. AI language analysis techniques are already being used to gather information on citizens the world over. Intelligence agencies have attempted to alter electoral outcomes by creating automated psychographic profiles of millions of individuals, and then targeting them in influence campaigns.

Daunting as they may seem, these threats emerge not from some sentient, superhuman genocidal AI hatching a Machiavellian plot to spell humanity’s doom, but from the application of artificial narrow intelligence (ANI) under human control. Today’s real threats are illustrated by more quotidian news: bias creeping into AI systems used for loan approvals, abusive bots that spew racial hatred and failed facial recognition algorithms that confuse humans with animals.

Furthermore, there are the nearly insurmountable competitive moats the largest data-driven technology companies have been able to create, virtually stifling competition. Now, emerging startups know their salvation lies in being acquired, not in attempting to compete with the incumbents. Leaders like Cisco Chairman Emeritus John Chambers have even cited the resultant reduction in IPOs as a threat to the strength of the U.S. startup ecosystem.

For business, what makes AI formidable is its incredible ability to concentrate power and business advantage in the hands of early adopters. The insights gained from data enrich products, enabling further use and more data in a rapid positive spiral, unleashed at scale and machine speed, that quickly locks out the competition. A recent McKinsey study even suggests that early AI adopters will build unassailable leads in their respective categories. Those left behind may never overcome the competitive advantages that accrue to first movers.

Whether for companies or countries, the greatest risk around AI today is one of exclusion. This exclusion manifests itself in the business landscape as increased potential for monopolies, and it manifests itself in the social landscape as growing inequality. In truth, the malevolent sentience immortalized in Hollywood’s “Terminator” films isn’t a near-term threat to anyone. But failing to integrate artificial intelligence into businesses and national defense may very well be. Those who fail to understand and embrace AI leave themselves vulnerable to an insurmountable disruption by those who come to grips with it first.

Indeed, disruption is a constant in the modern world, but the pace at which disruption unfolds becomes faster every year. The number of years a company stays on the S&P 500 shrank from 60 years in 1959 to 20 years today, and it is projected to be a mere 13 years by the mid-2020s. Prior waves of digital transformation have reshaped entire industries, but it is the coming cognitive transformation that will be the most profound.

What makes the coming transformation unique is that it will allow for infinite scalability of cognitive tasks, essentially reducing to zero the incremental cost of embedding “intelligence” in a product or service. Artificial intelligence isn’t just about doing business better; it’s about inventing entirely new ways of doing business. This can already be seen in the way that technology companies such as Google, Apple and Amazon are morphing into “everything companies.” The skill sets these companies bring to bear are not their unique mastery of retailing or logistics, but their ability to quickly find key insights from data and automate the thousands of individual processes needed to scale a business. It doesn’t matter to them whether the insights they seek relate to oil and gas, commercial real estate or the sales of Beanie Babies. Transforming data, insight and predictions into a product or service is where they excel.

It shouldn’t be surprising that these “everything companies” are launching aviation businesses, selling home appliances and developing cars. Leveraging AI, they will continue to expand their reach across industries, disrupting businesses that never considered them competition. Who would have predicted ten years ago that Amazon would one day own Whole Foods? Everything companies will use data and AI to compete with virtually anyone.

Of course, AI-powered disruption can be a double-edged sword. If they slow down or miss a market, even tech giants can land themselves in trouble. Much of the truly fearsome competition is likely to come from China. Even now, Chinese e-commerce firm is pushing into western markets, aided by AI technology that outstrips Amazon’s. is arguably the world’s leading company in delivery via autonomous systems—technology Amazon is still testing or only now rolling out in a few locations. It possesses the largest drone delivery system on the planet, as well as robot-run warehouses, drone “airports,” and driverless delivery trucks. And it’s not the only competition. Chinese ridesharing giant Didi boasts three times Uber’s global ride volume. With the ability to generate larger data streams, leverage a larger domestic user base and make use of massive government investments in AI, Chinese companies such as Alibaba, Tencent, Baidu, Weibo, Face++ and Didi may end up disrupting the disruptors.

As the ancient Greek adage proclaims, “change is the only constant.” In the age of AI, change is a super-exponential function. Today, it is not technology that should scare us. The only thing truly worthy of fear is our own inaction.


25 Jun 2019
Intelligence is not ‘artificial’: humans are in charge

Intelligence is not ‘artificial’: humans are in charge

Google CEO Sundar Pichai gave a surprising interview recently. Asked by CNN’s Poppy Harlow about a Brookings report predicting that 80 million American jobs would be lost by 2030 because of artificial intelligence, Pichai said, “It’s a bit tough to predict how all of this will play out.” Pichai seemed to say the future is uncertain, so there’s no sense in solving problems that may not occur. He added that Google could deal with any disruption caused by its technology development by “slowing down the pace,” as if Google could manage disruption merely by pacing itself — and that no disruption was imminent.

The term “artificial intelligence” often prompts this kind of hand waving. It has built-in deniability, with no definite meaning, and an uncertain impact always seeming to lie in the future. It also implies the intelligence belongs to machines, as if humans have no control. It distances Google and others from responsibility.

Artificial intelligence is here now — it is the software and hardware that surrounds everyone on earth. Humans are the architects, refining solutions of all kinds to make them perform intelligently. Designing systems that serve useful purposes is the intelligence; the rest is rote. It will be a long time before machines can identify new purposes and adapt solutions to them. For now, and for some time, machines will have considerable human help.

The term “advancing intelligence” might keep technology CEOs more accountable. Replacing artificial with advancing signifies the intelligence is human, not machine, and is guided by people working at technology companies. It widens the scope to the thousands of technologies — collectively intelligent — upon which people are already dependent, and signals that the future is a function of technology companies’ roadmaps by which their employees are (intelligently) building products to serve people in a multitude of ways. It also emphasizes advancement — social utility and its impact, not only the apparent aptitude of the machine.

Google’s CEO, then, could have acknowledged that Google is already a world leader in AI (advancing intelligence). AI is not only robots and autonomous vehicles, but information services that extend human intelligence — search, voice-recognition, mapping, news, and video services (YouTube), sharable documents, cloud storage, mobile access (Android), to name a few. With a mission statement “to organize the world’s information and make it universally accessible and useful,” Google has made a large fraction of the world’s information available to people with search, handling 6 billion requests every day.

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