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OECD issues report on AI in Society

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The OECD has published a detailed report looking at how artificial intelligence is affecting global economies and societies. The report considers the acceleration in AI investment and the growth of startups.

The new OECD report is titled “Artificial Intelligence in Society“, issued on June 11, 2019. The report charts the acceleration in artificial intelligence investment since 2016, particularly in the areas of Canada, China, the European Union, Israel and the U.S. As a sign of growth in investment, the report shows how artificial intelligence startups have attracted 12 percent of global private equity investments in the first half of 2018, a figure that is up from just three percent in 2011.

The report additionally looks at the different sectors adopting artificial intelligence systems, the policy challenges of the technology, and issues like risk management and approaches to transparency. This is tune with the focus on how the technology affects society found in the report.

While it is acknowledged that artificial intelligence adoption can generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises; at the same time, artificial intelligence applications raise questions and challenges related to human values such as fairness. There are also considerations of human determination (self-will), privacy, safety and accountability.

The report follows on from the OECD, together with partner countries, formally adopting a new set of ‘Principles on Artificial Intelligence’. This activity took place at the Organization’s 2019 Ministerial Council Meeting on May 22 in Paris.

The goal of the new report is to help to construct a shared understanding of artificial intelligence and to encourage dialogue on important policy issues, like labour market developments and upskilling for the digital age. There are other issues like privacy, accountability and the responsibility, security and safety questions that artificial intelligence generates. These form part of a section of the report titled “Public policy considerations”.

An interesting section of the report, which runs to 152 pages, it titled “AI applications”. This section considers ten areas that are seeing a rapid uptake of artificial intelligence technologies. These areas are: transport, agriculture, finance, marketing and advertising, science, health, criminal justice, security, the public sector and augmented/virtual reality. The report looks at the benefits in these areas such as raising the efficiency of decision making, saving costs and enabling better resource allocation.

The report also looks at the big investment in startups, which is running somewhere between $26 billion to $39 billion, with three-quarters of this investment coming from big technology companies – primarily: Google, Apple, Baidu, Facebook, Amazon, Intel, Microsoft, Twitter and Salesforce.

The biggest sectors within which artificial intelligence startup companies are developing are cybersecurity (for example, Amazon and Oracle purchased Sqrrl and Zenedge, respectively); autonomous vehicles; and healthcare. While China and the U.S. are the biggest areas for startup investment (ahead of the European Union in third place), the most rapid growth has occurred in Israel, which has seen the share of investments in AI startups jump from five to 25 percent between 2011 and the first half of 2018.

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Retail

IoT + Data Analytics = Store Operations Intelligence

How many times have you visited a grocery store the day before a snowstorm or other major weather event only to find the bread and milk aisles wiped clean? What might be a disappointment for you is also a missed opportunity for grocery stores, an industry with an already razor-thin 2% margin.

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How many times have you visited a grocery store the day before a snowstorm or other major weather event only to find the bread and milk aisles wiped clean? What might be a disappointment for you is also a missed opportunity for grocery stores, an industry with an already razor-thin 2% margin.

Hungry for efficiencies

Inventory management, especially for perishables, is a delicate dance. Too little of it and grocers have lost a revenue opportunity every time a customer leaves empty-handed. Too much of it and grocers lose revenue again, this time from spoilage or having to slash prices to clear shelves. Spoilage is a significant problem — grocery retailers lose an astounding $70 million annually because of food simply going bad. 

Market economics further muddies the picture. A whopping 82% of grocery companies are increasing their stock of fresh foods in response to customer demand so there’s simply more perishables to manage — and therefore more at stake.

To ensure not too much capital is tied up in unsold goods, grocery stores forecast demand and supply based on a variety of conditions, including weather, time of year, and even weekly foot traffic. But as Cognizant as observed, a whole host of additional factors affecting inventory management can drain grocery store revenues.

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Smart systems

One of Cognizant’s clients, a major supermarket chain, found that working with older equipment also challenged inventory management. 

Internet of Things (IoT)-embedded sensors track ambient temperature, temperature of the food, humidity and even electric current flowing into refrigerators to keep a pulse on perishables. But this leads to grocery stores drowning in data. The sensors cry wolf too often forcing the retailer to waste expensive technician time on every perceived crisis. Such waste happens because too often, sensors do not accurately reflect the whole story. 

Cognizant has shown that data alone is not enough, strategic reading of the data tea leaves also matters in increasing efficiencies. Using the IoT sensors, Cognizant helped the grocery retailer monitor inventory in real time — the pressure on sensitized shelves changes when inventory counts drop — and restock accordingly. Even better, Cognizant’s solution analyzed the data feed in real time, at the edge. Algorithms accounted for many variables including work load, cost of energy at different times of the day, whether the door was open or closed, to recommend intelligent solutions. 

Using edge data analytics and IoT sensors, grocery stores can automate many fixes, proactive reorder inventory and even automatically churn out work orders for technicians only as and when needed.

When inventory management is a delicate and challenging operation, grocery retailers need to be strategic about how they invest precious resources. IoT + edge analytics is a game-changer. It gives retailers the intelligence they need to deploy resources effectively and proactively so they can better cater to demand and cut waste. 

IoT-driven asset management and data analytics will be key to success in the grocery industry. Climate change has increased the clamor for sustainability and less food waste. The timing for smart solutions could not be better.

Read more about Cognizant’s IoT refrigeration solution here.

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IoT + Big Data = Facilities Management Intelligence

In the equation IoT + X = Operations Intelligence, what role does big data play in facilities management?

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The way we work today has changed. The workforce is becoming mobile and companies rent temporary space depending on needs.

At the same time, facilities management professionals have a number of mandates, says Nancy Berce, chief information officer at Johnson Controls. They need to control costs while still delivering personalized experiences. They need to regulate access so only authorized personnel can enter key areas of buildings. They need to conform to wider regulations imposed by the pressing concerns of climate change.

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The Internet of Things (IoT) helps deliver such efficiencies by helping facilities management professionals harvest and analyze big data — smarter and at scale.

The parallel evolution of big data and IoT

Facilities management professionals have monitored heating and cooling systems and fire and security systems for decades. But IoT has delivered a fundamental shift in how that monitoring occurs. IoT-embedded devices can relay health of the equipment in real time and the big data from such equipment help professionals manage facilities on a much more granular level.

No more blindly replacing all the light bulbs every six months. A digital-enabled platform can alert professionals to systems that are in danger of failing so the appropriate actions kick into action only as and when needed — with minimum cost to the facility and minimum disruption to the worker. “We now have a level of intelligence and insight from smart algorithms where we can be proactive about preventative maintenance and predict efficiency opportunities a lot sooner,” Berce says. 

How to leverage big data and IoT

Johnson Controls’ Bee’ah green building project, illustrates just how smart IoT-driven buildings can drive efficiencies at scale to deliver a nearly fully automated workplace of the future, with temperature and lighting controls just a few of the parameters that adjust depending on workforce distribution. 

IoT increases the number of data sets that facilities can play with and allows them to pinpoint trouble before it brings down the entire system. How does one leverage the benefits of big data and IoT to deliver intelligence?

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

First, connecting all the big data points together is key to see the larger picture, Berce says. Companies might already have the information they need for smarter operations, but they might be in silos. IoT data related to security, for example, can be connected to an active employee directory, to automate entry to more sensitive areas of buildings (think operating rooms in hospitals). Companies can even marry IoT systems with external weather data to manage their cooling systems.

Second, understand the insights you are looking for and use IoT accordingly, Berce says.

Third, retrofit legacy systems with IoT devices as needed. 

Finally, make the data analysis easy to visualize, advises Berce. A digital platform where professionals can easily detect anomalies makes it better to find the needle in the haystack and act on the intelligence that big data and IoT are delivering. 

IoT and big data allow professionals to do all things at once — to both zoom in and zoom out as needed. Such flexibility allows facilities management to meet the growing demands for efficiency while customizing personalized experiences for each and every worker.

[Download]: A New Approach to PLM

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IoT + Smart Edge Computing = Operations Intelligence

In the equation IoT + X = Operations Intelligence, what role does smart edge computing play?

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You don’t always need a sledgehammer to crack a nut.

The general premise driving the use of the Internet of Things (IoT) and data analytics to deliver intelligence is that the end actions usually have to be executed through some kind of blanket (often human) intervention. The shaky fallacy at the core of this idea is that it takes a sledgehammer to a nut in that even small adjustments to operating conditions requires a large investment of resources. Smart edge computing addresses this challenge and applies a solution that is more proportional to the size of the problem.

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Whether it’s a thermostat or a light switch or a card reader, most edge devices that control today’s commercial facilities are passive and wired devices, says Datta Godbole, the chief technology officer for Honeywell Building Technologies. Smart edge computing introduces a more efficient way of corralling the power of IoT to deliver operations intelligence. Smart edge devices can act on intelligence on the frontlines and save the heavy-duty computing for the cloud.

Smart edge computing helps companies, including facility management organizations, distribute computing needs more efficiently: you execute the small changes at the edge and save the heavy lifting for the cloud. “Time critical decisions are executed quickly without going to the cloud, while cloud computing is great for analyzing long-term trends through AI algorithms,” Godbole says.

Decisions at the edge

It is this “quickly” factor, the latency that is saved, that makes smart edge computing so valuable as part of the equation IoT + smart edge computing = operations intelligence.

Imagine a commercial building packed with fire and smoke detectors. Facilities management needs to maintain and periodically inspect these devices, which involves days of intensive work. What if instead the smoke detector could signal when it’s ready for maintenance – much like your car does? “In the future, all equipment in the building will be smart and can diagnose themselves and ask for help,” Godbole says.

The IoT part of the equation comes from the many sensors measuring a variety of parameters including temperature, humidity, light, foot traffic, occupancy and more. The introduction of IoT expands the working data set so management can more finely calibrate the final experience. “If we have IoT sensors that blanket a whole building, that conduct micro-measurements of every part of the building, we get a much truer picture of what’s happening in the building and you can control air conditioning or heating accordingly,” Godbole says.

Case Study: Advancing Smart Manufacturing Operations Value with Industry 4.0 Platform

In a sense, IoT allows for both personalized comfort and efficiencies at scale. When an employee swipes her card and enters her workspace, what if IoT-embedded edge devices automatically gave her what she was looking for: a slightly warmer conference room, lighting that adjusted depending on where she was working and her favorite snacks lined up in the kitchen?

Foot traffic sensors and occupancy patterns in the long term can dictate heating and cooling requirements so management can optimize these over time.

The use of IoT in conjunction with smart edge computing will lead to a more efficient allocation of computing resources and better and faster decision-making. No longer do you need a sledgehammer for every problem, a fine scalpel will work even better.

[Download]: A New Approach to PLM

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