British artificial intelligence company Wayve has raised more than $1 billion to develop self-driving tech.

The London-based start-up confirmed a series C investment round of $1.05 billion that will be used to evolve “Embodied AI” products for autonomous driving.

It’s the largest investment to date in a European AI start-up, with big-name backers including Japan’s SoftBank – which led the funding round – Nvidia and Microsoft.

As we have seen from the many companies developing self-driving products around the world, there are different approaches to delivering autonomy. Wayve’s solution has more of a reliance on AI than some, is hardware-agnostic and does not use mapping.

Embodied AI is described by the firm as an integration of “advanced AI into vehicles and robots to transform how machines interact with, comprehend and learn from human behavior in real-world environments.”

Wayve claims to have been pioneering this approach since its foundation in 2017, and says it is the first company to develop and test an end-to-end deep learning autonomous driving system on public roads.

It’s already attracted some famous fans, including Bill Gates, who enjoyed a “memorable” ride in a Wayve AV in London last year.

Currently, Wayve is building foundation models for autonomy that it describes as like a “Chat GPT for driving.” These will allow vehicles to see, think and drive in any environment and in the longer term, the company’s plan is to launch products that can be applied to vehicles from OEMs, although it does not have any deals in place as of yet.

These products will allow automakers to upgrade cars from Level 2 driver assistance to Level 4 full autonomy, as defined by the Society of Automotive Engineers.

One advantage of the Embodied AI approach, says Wayve, is that it will endow autonomous driving systems with the intelligence to confidently navigate so-called edge cases – scenarios which do not follow set patterns and which a human driver, for example, would generally respond spontaneously to.

These have traditionally caused problems for autonomous vehicles (AVs).

Automakers (and fleet owners) will also be able to leverage Wayve’s proprietary tools to generate data for further learning, while the company’s research on multimodal and generative models – known as LINGO and GIAI – will eventually “offer advanced features like intuition, language-responsive interfaces, personalized driving styles and co-piloting.”

The funding, which follows a $200 million round in January 2022, was hailed by Wayve CEO Alex Kendall.

“At Wayve, our vision is to develop autonomous technology that not only becomes a reality in millions of vehicles but also earns people’s trust,” he said. “This significant funding milestone highlights our team’s unwavering conviction that Embodied AI will address the long-standing challenges the industry has faced in scaling this technology to everyone, everywhere.

“This investment will enable us to develop and launch our first Embodied AI products for the automotive industry, empowering OEMs to provide consumers with trustworthy and beneficial automated driving experiences.”

Recognizing the importance of the deal for the U.K., Prime Minister Rishi Sunak added: “The fact that a homegrown, British business has secured the biggest investment yet in a U.K. AI company is a testament to our leadership in this industry.”

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“5G will inevitably shake up the media and entertainment landscape. It will be a major competitive asset if companies adapt. If not, they risk failure or even extinction. This wave of 5G transformation will not be the purview of any singular industry, and now is certainly the time for all business decision-makers to ask: Is your business 5G-ready?”
–Jonathan Wood, general manager of Business Development & Partnerships, 5G Next Generation and Standards at Intel

Why 2025 is the 5G Tipping Point: The report says that as early as 2025, 57 percent of global wireless media revenues will be generated by using the super-high-bandwidth capabilities of 5G networks and the devices that run on 5G. The low latency of these networks means that no video will stall or stop – livestreaming and large downloads will happen in the blink of an eye.

The report points to the following breakouts in revenue as 5G networks overtake 3G and 4G by offering new capabilities:

• 2022: nearly 20 percent of total revenues – $47 billion of $253 billion
• 2025: more than 55 percent of total revenues – $183 billion of $321 billion
• 2028: nearly 80 percent of total revenues – $335 billion of $420 billion

How Media Demand Drives Network Evolution: The “5G Economics of Entertainment Report” forecasts that 5G will accelerate content consumption, including mobile media, mobile advertising, home broadband and TV, and improve experiences across a broad range of new immersive and interactive technologies – unleashing the full potential of augmented reality (AR), virtual reality (VR) and new media.

The average monthly traffic per 5G subscriber will grow from 11.7GB in 2019 to 84.4GB per month in 2028, at which point video will account for 90 percent of all 5G traffic.

Evolved 3G and 4G networks would offer a degraded experience because the capacity will be insufficient to handle the increased video viewing time, content evolution to higher resolutions, more embedded media and immersive experiences.

Forecast to provide $140 billion in cumulative revenues (2021-2028), expanded AR and VR experiences may also enable a whole new channel for content producers to reach consumers.

Immersive and new media applications – applications and capabilities that are currently nonexistent – will reach unprecedented scale by 2028, forecast to generate more than $67 billion annually or the value of the entire current global mobile media market – video, music and games – in 2017.

How Businesses are Tuning in to 5G: Beyond media and entertainment, all industries are having to adapt to disruptive changes in the business environment, consumers’ habits and public expectations. Businesses are trying to imagine how 5G will transform and disrupt their industry, society and even global competitiveness, as well as beginning to formulate their strategy to embrace the capabilities of 5G. Without the promise of what 5G can provide, growth in many industries may remain stagnant or even decline.

When 3G wireless networks launched, no one could have predicted how the mobile world would look today. 4G in the U.S. spawned entire new industries. Companies like Airbnb*, Uber*, Netflix* and Spotify* may not have been possible without 4G technology.

“The big question is: What will not be impacted or disrupted by 5G? The next generation wireless network will power diverse digital innovation – everything from the computerization of physical objects to artificial intelligence, ushering in an exciting new world that business leaders and indeed nations need to prepare for,” said Ed Barton, chief analyst of the Entertainment Practice, Ovum.

What It Means to Intel: 5G networks will provide lower latency (to be more responsive), greater speeds (to move the increasing volumes of data we produce), and the ability to expand beyond computers and phones to encompass the cloud and a whole new universe of devices (estimated in the billions) attached to the network. Intel is uniquely positioned to power the entire 5G tech ecosystem to support this flood of new data.

Intel is no longer just “inside” your computer; Intel’s technological innovation is expanding to 5G network, consumer and industrial applications and the cloud.

More Context: E-Book: How 5G will Transform the Business of Media & Entertainment | Backgrounder: Key Findings — 5G Economics of Entertainment Report | Infographic: Immersive 5G Experiences Start and End with Intel | Infographic: 2025: The 5G Tipping Point | Press Kit: 5G at Intel | Intel.com: Intel 5G: Network. Cloud. Client. | Article: Joint Venture with Intel-Funded Startup Eko Makes Walmart a Major Player in Interactive Media

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Today’s businesses see market data as a commodity. Readily accessible information about consumer activity and preferences allows market researchers to develop large data sets to mine for consumer insights. And indeed, a look through recent market research industry publications shows that discussions in the field have been dominated by a focus on data analysis.

But more often than not, insight into what customers really care about is hampered by the quality of the data being collected. Some market researchers conflate the idea of data quality with sample size, with the belief that reliability, validity, and other characteristics of “good measurement” derive solely from the amount of data collected. This is certainly not the case.

A heavy emphasis on data collection and analysis is irrelevant if it omits the first and most important step of market research — the design of the metrics. In the psychometric tradition, survey development and the construction of specific survey questions has been emphasized as the most important step in the research process. Unfortunately, this step is getting short shrift by most market researchers today.

Failing to assess the measures that are the foundation of business decisions poses a colossal risk. Making data-driven decisions based on poor measures can be infinitely worse than making decisions without data at all.

To help organizations think more critically about the measures they use to collect information about consumers, we’ve outlined four common misconceptions held by many market researchers and provide suggestions for how to break away from these mistaken beliefs.

Belief in Measurement (Without Thinking About What We’re Measuring)

Historically, when market researchers wanted to measure a construct, such as how consumers feel about a particular brand (for example, “brand love”), they would ask respondents to rate questions that directly describe the construct, such as “How much do you love this brand?”

This kind of “measurement by describing” has its share of problems. For instance, many constructs are too abstract for regular consumers to report on in concrete terms. Think about how you’d reply if you were asked how much brand love you have for Tide laundry detergent. Most people couldn’t get more specific than reporting general approximations such as “a lot” or “a little.”

Researchers have begun to move toward methods that use self-reported data in better ways. Instead of asking, “How much do you love this brand?” today’s best practice is to pose statements that a consumer might endorse if they loved the brand. For example, “disagree/agree” statements like “I would drive 20 miles to purchase [Brand]” would be fully endorsed only if the respondent really loved the brand. We derive the level of the construct from behaviors respondents say they would engage in.

The “measure by deriving” approach requires a deep conceptual understanding of what is being measured. But many market researchers still ask questions the old way, descriptive of what’s being measured (“I like the ad I just saw”) rather than descriptive of derivative behaviors (“I showed the ad to friends”).

If market researchers continue to write surveys that measure constructs overtly instead of by their derivative behaviors, the data will likely be subject to uncertainty and error that could easily be avoided. They need to put more thought into what exactly they want to know, carefully consider what behaviors should be the consequence of that construct, and develop the measure from there.

Belief in the One-Question Measure

Market research is not exempt from the financial pressures of business. Cost-consciousness trickles into our work when clients ask for short surveys that cover as many topics as possible — which often results in a single question for each topic. For example, “How satisfied are you with your experience?” might be the only question in a survey that assesses “customer experience.”

There are a number of challenges with this kind of thinking. First, the single question might not be a unique measure of “customer experience” but instead a measure of some other construct such as “agreeableness.” It is not uncommon in the world of psychometrics for items to “cross load,” meaning they can be a measure of more than one thing. Second, assuming that a question does measure what we want it to measure, it rarely ever measures all aspects of a construct. In the example of customer experience, many aspects — including price, promotions, interaction with employees, and perceptions of the brand — join to influence an individual’s global impression of his or her experience. Asking just one question fails to assess these multiple aspects and neglects the variations in people’s impressions.

As well, limiting a measure to only one question doesn’t always allow an organization to measure the change in a respondent’s impressions, because it forces a measurement ceiling on respondents. Imagine a scenario where an organization wants to test new ads by measuring the effect the ad has on brand impression. Respondents who already love the brand might have “maxed out” on their measure of brand impression if they gave the brand a 7 on a seven-point scale prior to ad exposure. Even if they’re shown really good ads, they can’t increase their score in the post test — they can only stay at a 7. This limitation to the measure does not correspond with the way reality works: More often than not, brand loyalists’ passion for a brand can be increased with ads and brand actions that resonate.

Having multiple questions that measure different aspects of the same construct is a fail-safe way to make sure data-driven decisions are actually based on all of the aspects of the construct of interest. It’s a way to capture all of the information that the measure can supply.

Belief That All Questions Are Created Equally

Even if market researchers heed the advice to use multiple questions for each construct they are measuring, they must remember that different questions provide different information.

Consider the standardized tests that are used widely in the U.S. for college admissions. The SAT and ACT are designed to include easy items that most test-takers will answer correctly and difficult items that fewer can answer correctly. Market research questions work in a similar manner. There are some behaviors that almost anyone would do whether they love a brand or not, such as read some of the brand’s posts on social media. There are other behaviors that only those who really love the brand would do, such as spend a large portion of their disposable income on it or travel a considerable distance to purchase it.

This point can be illustrated visually. (See “Easy- and Harder-to-Endorse Statements Provide the Whole Picture.”) For example, assume that the construct (also called latent trait) “brand love” ranges from -4 to 4. People who dislike the brand are on the lower end and people who love it are on the higher end. Respondents in the middle, at 0, have a 51% probability of endorsing the “easy” statement of “I would read [Brand’s] posts on social media” and only a 6.8% probability of endorsing the “difficult” statement of “I would drive 20 miles to purchase [Brand].” A higher level of brand love is required to endorse the more difficult statement. In this example, a respondent with a higher latent score of 1 has a 75% probability of endorsing the easy item and jumps to a 90% probability of endorsing the hard item.

Marketers need to select questions and statements with a range of difficulties to ensure that useful information can be captured from all respondents.

Belief in Using Scales Without Scaling

One of the most important jobs that market researchers have when doing research is to provide an interpretation of what they are measuring.

One way to do this is by using norms, which are created when a researcher has collected the same measurement on a number of individuals or groups. By knowing how others score, researchers can see where an individual falls relative to everyone else. Another approach is criterion-reference scaling. For example, tests for a driver’s license are criterion-referenced, where “proficiency” is established using a cut-off score (for instance, getting 85% of answers correct is required to pass). Rather than considering how an applicant scores relative to others, the focus is on whether the driver is proficient based on scores that are predetermined by state agencies.

But while both norms and criterion-reference approaches help aid with the interpretation of a score relative to other people (via norms) or relative to some other external criterion (via criterion-reference), they do not aid in the interpretation of how an individual scores relative to the latent trait being measured — in the example “Easy- and Harder-to-Endorse Statements Provide the Whole Picture,” that would be how much an individual dislikes or loves a brand to begin with on the scale of -4 to 4. Pairing how individuals score on a measure with their level of the latent trait is a step that market researchers often skip.

Imagine you’re analyzing survey results about “brand loyalty” looking at data from a five-question survey, where each question was ranked using a seven-point Likert scale. If respondents gave the questions an average score of 5 (or a sum score of 25), how would you interpret that?

The truth is that you simply might not know what constitutes a high score without scaling. Some might see this as a bad score because it is far from the maximum they could have gotten — an average of 7, with a sum score of 35. However, just because the scale maximum is 35, that doesn’t mean that a respondent who exhibits a high level of brand loyalty will say 7 across every question. It could be that a 25 is a high score on this scale.

Regardless of what they’re measuring, market researchers must recognize that scaling is a necessary step. While most market researchers use norms, it is of increasing importance to use statistical models such as item response theoryto establish a correspondence between responses on a measure and level on the latent trait.

Measurement: There’s More Than Meets the Eye

Measurement is a tough thing to get right. The more we work with clients and their vendors, the less emphasis we see being put on how measures are created. While big data gives us safer ground for generalizing our results, it is no substitute for the careful crafting of a measure that has been tested for reliability and validity.

By thinking carefully about what is being measured and adopting psychometric best practices, researchers and executives can make data-driven decisions that stand on the strongest possible footing.

This article appears in the October 2018 print issue as “MIT Sloan Management Review”

ABOUT THE AUTHORS

Ken Faro is a senior manager of research in the department of decision science at Hill Holliday, a Boston-based advertising company. He tweets @kennethrfarophd. Elie Ohana is a researcher in the department of decision science at Hill Holliday.

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The internet of things (IoT) is digitally transforming businesses before our eyes. Throughout the past few years, companies across industries embarked on their IoT journeys with high hopes, focusing on improving and automating existing processes – tackling the ‘low-hanging fruit’.

Now that these companies have gained process, productivity and cost benefits, it is time to move to the next phase: leveraging IoT to create new value propositions, business models and revenue streams.

However, it is increasingly difficult for businesses to experience the full transformational potential of IoT if they implement it on top of infrastructure designed years ago for specific tasks. In fact, 85pc of businesses decision-makers cite legacy infrastructures as a key reason they are not reaching their full digital transformation potential.

As the technological foundation for your IoT deployment – comprising smart devices, sensors and actuators; wired or wireless networks; software, middleware for data management and more – your infrastructure should take full advantage of modern technology advances to enter the 21st century.

Traditionally, most organisations have implemented IoT within brownfield environments, encompassing specialised systems, often operating in isolation, based on decades-old protocols. These elements often lack the flexibility, speed, bandwidth, security and interoperability required to capitalise on the full capabilities of IoT technologies.

But, as we finally move to the next, more disruptive phase of IoT, businesses must address their insufficient infrastructures to remain competitive, no matter where they are in their IoT journeys.

The business value of a modern infrastructure

Completely ‘redoing’ your company’s infrastructure may sound daunting and costly, but it doesn’t have to be. And you don’t have to overhaul it all at once – it is possible to take a pragmatic approach and insert elements of a 21st-century architecture into existing workflows. In other words, you’re establishing the building blocks of the modern, end-goal architecture.

If you’re still wary of making these changes, consider the long-term benefits and business value. A new infrastructure will set you up for a faster, more significant ROI because the resulting IoT systems are far less complex, and optimised for latest technologies and applications. When based on open standards, these ‘digital transformation-ready’ systems enable interoperability so that businesses can quickly and easily introduce new solutions and capabilities that add leapfrog value now or later down the road, at a lower cost.

Moreover, you are future-proofing the infrastructure to take advantage of the 75bn connected devices that are expected to emerge by 2025, and the myriad use cases they will bring. Further, you’ll be able to integrate IoT more easily with other rising technologies, such as artificial intelligence/machine learning, fog computing and blockchain. You can even implement these technologies gradually once you have a design for your overall framework and architecture in place.

Now, consider the costs and expected ROI of an IoT implementation in a greenfield versus brownfield environment. IDC projects that a digital transformation-ready network, when combined with the right technology solutions and services, delivers an average ROI of 402pc over five years, with payback in just nine months. That breaks down into an average of $48,117 in savings per 100 users, or $3.1m per organisation, per year.

A real-world revolution

Look no further than PepsiCo for an example of success with an infrastructure overhaul. Instead of introducing IoT to a hodgepodge of outdated and inefficient technologies, PepsiCo replaced its existing infrastructure, adopting an ‘infrastructure-as-a-service’ model.

Working closely with partners Rockwell Automation and Cisco, PepsiCo adopted virtual industrial servers and a standard network infrastructure, coupled with centralised expert support on a pre-engineered, scalable server infrastructure. The solution included all hardware, software and network connectivity preconfigured to support PepsiCo’s unique business needs.

As a result, PepsiCo increased the reliability of plant manufacturing systems, while reducing support costs. Further, the IoT deployment allows support staff to communicate with all components of the infrastructure through continuous remote monitoring. This enables technicians to proactively address any issues before they impact production. The ROI from these revolutionary changes was almost immediate, and PepsiCo also reduced troubleshooting time by 90pc – meaning far less downtime.

How to get started on your IoT infrastructure upgrade

As mentioned, your infrastructure overhaul doesn’t have to be an overwhelming endeavour. Here’s how you can get started.

Define the business transformation

Too often, businesses get caught up in the hype of IoT and its ‘cool’ technologies and lose sight of its transformational value. Before investigating a new infrastructure or exploring IoT technologies and connections, define your use case and your desired end state. What business problem do you want to solve with IoT? And what ROI are you hoping to achieve?

Analyse your current infrastructure and design your new one

Identify what’s outdated and what’s state-of-the-art. What internet protocol (IP) are you using? How ‘intuitive’ is your network, or is your entire IP infrastructure (including switches, wireless access points and routers) securely connected on a unified platform? From there, you can design your overall, end-goal architecture, comprising flexible frameworks and leading technologies (IoT, AI, fog computing and blockchain).

Make a decision

Next, decide whether you opt for a one-time revolutionary overhaul or take a more incremental approach. Or maybe it makes sense at this point to simply retrofit your existing system with IoT, and that’s OK, too. Here, keep in mind the ROI and ensure your decision aligns with your business’ long-term goals. However, you shouldn’t make that decision alone.

Leverage your ecosystem’s expertise

IoT is a team sport. No single company can ‘do’ IoT by itself – it is too costly, time-consuming and complex, resulting in limited payback or even failure. As you determine the right approach for preparing your infrastructure for digital transformation, seek out the advice of your partner ecosystem. That includes horizontal and vertical providers, along with hyper-local regional experts, to ensure your IoT project (and results) meets your specific business need.

As this next disruptive phase of IoT progresses, incremental improvements to existing processes will not be enough to keep companies competitive. Whether you are starting your first IoT project or one-hundredth, take time now to assess your existing infrastructure. It may very well be holding you back. By bringing your infrastructure into the 21st century, you’ll be ready to take full advantage of the transformational power of IoT.

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The investigation into the Cambridge Analytica scandal was a high point for awareness of privacy breaches in the social media community, but it certainly was not the first. In February 2018, Guillaume Chaslot, a former YouTube employee, went public with his study on YouTube’s algorithms, which found extreme bias in relation to the 2016 election. The study found that 84% of videos recommended by the algorithm were pro-Trump, with only 16% pro-Clinton. Meanwhile, Twitter came under attack as a documentary by Project Veritas purportedly proved political bias in its regulation of its users.

The push for better regulation with regard to how algorithms work and how to protect user privacy has already advanced, with the European Union’s General Data Protection Regulation (GDPR) governing online data privacy and use of user data having gone into effect in May 2018. However, we contend that while these efforts have been aimed at regulating user data, efforts must be made to regulate algorithms themselves.

Algorithms Are More Than Just Social Media

The truth is, algorithms pervade our lives. They have existed in the systems that run and regulate our lives for decades, performing tasks from a national security early warning system to traffic control systems. More recently, algorithms have found their way into our cars, our homes, and now have tasks as varied as deciding how suitable we are as job candidates or helping to identify health issues.

As with social media, while these algorithms have delivered convenience and usability, they have also failed us. In March and April 2017, Tesla was hit by two lawsuits by citizens claiming that Tesla’s autopilot function was “dangerously defective.” Risk-assessment algorithms regularly used to predict likelihood of reoffending in criminal offenders in six states throughout the United States have been found to be significantly racially biased, resulting in black offenders receiving longer and heavier sentences than their white counterparts. Tay, Microsoft’s machine-learning chatbot, was taken offline within 24 hours of its launch on Twitter after its conversation and language patterns became disturbingly racist.

The risk to users of technology reliant on algorithms is about more than just privacy concerns. There is risk associated with the algorithms themselves — the purpose for which they are built and their error rates in fulfilling these purposes.

We contend that there is a mismatch between the purpose of the algorithms and the rigor with which the algorithms are tested for efficacy, and the impact of a failure in the algorithm. For example, a minor failure in Tesla’s autopilot function to properly operate in certain conditions could result in numerous injuries and deaths if it fails just a fraction of the time. An element of racial bias in algorithms involved in sentencing criminal offenders would reinforce racial discrimination and serve to prevent minor offenders from rehabilitation.

What Do We Do Now?

It’s clear that we have come to the point where the risks of relying on algorithms are becoming too large to ignore. Politicians and journalists have already begun calling for regulation of social media algorithms, even as the platforms continue to mine user data and defend their algorithms. As of May 2018, 33 states in the U.S. had already introduced regulations relating to self-driving cars, but not their algorithms.

But how do we even begin to approach the regulation of a technology that is so widespread and widely used? Are all algorithms equally dangerous? Adding to the complexity of regulation is the fact that algorithms are typically considered proprietary technology, and different algorithms with different uses are governed by different agencies. For example, self-driving cars are governed by transportation authorities, while medical algorithms for disease detection or risk assessment are governed by the Food & Drug Administration. Some tech organizations don’t believe it’s possible to fully regulate algorithms and have argued that this kind of regulation would be a goal that is beyond the capabilities of a government.

An Approach for Assessing and Managing Algorithmic Risks

We propose that a combination of government regulation and self-regulation be introduced as a balanced approach that both protects proprietary assets and helps manage the impact of algorithms on our lives. This approach will also allow for some level of transparency and building an element of trust with the global community.

Government regulation. While algorithms remain valuable proprietary assets for technology companies, there is an inherent need for greater transparency and understanding of how those algorithms work. In his book The Black Box Society, Frank Pasquale recommends allowing a greater role for regulators such as the Federal Trade Commission (FTC) to test algorithms for ethical values such as fairness, social bias, and anti-competitiveness. This should be supported by the requisite funding and would require the ability and willingness to prosecute for ethical violations in the same way that the financial system is regulated. In fact, starting with social media, the FTC has shown a willingness to ensure that algorithms reflect accurate and fair information. For instance, in October 2009, the FTC revised its Endorsement Guides to encompass blogging, and, more recently, the FTC has further flexed its regulatory muscles in the social media sphere, now ensuring that social media influencers comply with these same kinds of regulations.

Regulations to ensure consumer protection from the use of algorithms in technology are on the horizon and cannot come soon enough. In January 2017, the Consumer Product Safety Commission produced reports on the safety of emergent and future technologies, which highlights the state of the art on the use of algorithms to draw insights from consumers or control the behavior of robots and digital assistants. In 2018, the FTC invited public comment and began conducting a series of public hearings on issues arising from the use of digital technologies and algorithms that are likely to help in the development of policies. For example, the FTC received comments from the Information Technology & Innovation Foundation (ITIF) on “the consumer welfare implications associated with the use of algorithmic decision tools, artificial intelligence, and predictive analytics,” in which the ITIF highlights the inadequacy of existing regulations and the need for regulators to protect individuals from companies using algorithms.

Self-regulation via an industry body and an ethical framework. We recommend that technology companies construct an industry-wide ethical framework that applies to algorithms to address fairness, social bias, and anti-competitiveness. This can be constructed by an industry body tasked with constructing the framework and best practices to be implemented, as well as light enforcement in the form of identifying companies that are aligned with their standards. Examples of successful implementations of a similar approach in other industries are numerous, including the palm oil industry with RSPO certification, the fair trade model in agriculture, and the Made in USA standard.

An industry can use this approach to introduce a method to self-regulate its own proprietary algorithms. This also aligns with the previously proposed approach to government regulation, which requires both self-assessment on the part of the technology firm and enforcement by government regulators.

Self-regulation via a risk assessment and prevention framework. In discussing the risks of technology, in his 2010 book The Technology Trap, L.J. Dumas highlights the need to assess the maximum credible risk of technology used on a high-volume basis. The implication is that, when a technology is used at high volumes, even rare events become more likely to occur.

We propose that technology companies use a similar approach to construct a risk assessment framework to apply to algorithms. The aim is to identify critical risks that take the volume of the algorithm’s users into consideration and assess the likelihood of critical events — that is, events that can be considered disastrous. Companies should use the results of such a framework to construct a comprehensive risk prevention framework to minimize the likelihood and impact of critical events.

As news continues to surface of algorithms exploiting public opinion and the impact of algorithmic failures, the need for regulation becomes clear. Nevertheless, consumer awareness, responsible business practices, and governmental protection are still no match for the threat algorithms present to our privacy and social equality.

Important steps toward upholding the legal and ethical principles of democratic societies in the digital age include organizations’ efforts to incorporate values and transparency into their algorithms, and government regulations that encourage innovation and accountability. Finally, it’s important to delineate clear penalties to those who use algorithms with disregard for or intent to harm.

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“Alibaba’s highly innovative data-centric computing infrastructure supported by Intel technology enables real-time insight for customers from the cloud to the edge. Our close collaboration with Alibaba from silicon to software to market adoption enables customers to benefit from a broad set of workload-optimized solutions.”

– Navin Shenoy, Intel executive vice president and general manager of the Data Center Group

What the Headlines are: Intel and Alibaba Group are:

• Launching of a Joint Edge Computing Platform to accelerate edge computing development
• Establishing the Apsara Stack Industry Alliance targeting on-premises enterprise cloud environments
• Deploying latest Intel technology in Alibaba to prepare for the 11/11 shopping festival
• Bringing volumetric content to the Olympic Games Tokyo 2020 via OBS Cloud
• Accelerating the commercialization intelligent roads

“We are thrilled to have Intel as our long-term strategic partner, and are excited to expand our collaboration across a wide array of areas from edge computing to hybrid cloud, Internet of Things and smart mobility,” said Simon Hu, senior vice president of Alibaba Group and president of Alibaba Cloud. “By combining Intel’s leading technology services and Alibaba’s experience in driving digital transformation in China and the rest of Asia, we are confident that our clients worldwide will benefit from the technology innovation that comes from this partnership.”

How They Accelerate on the Edge:

Intel and Alibaba Cloud launched a Joint Edge Computing Platform that allows enterprises to develop customizable device-to-cloud IoT solutions for different edge computing scenarios, including industrial manufacturing, smart building and smart community, among others. The Joint Edge Computing Platform is an open architecture that integrates Intel software, hardware and artificial intelligence (AI) technologies with Alibaba Cloud’s latest IoT products. The platform utilizes computer vision and AI to convert data at the edge into business insights. The Joint Edge Computing Platform was recently deployed in Chongqing Refine-Yumei Die Casting Co., Ltd. (Yumei) factories and was able to increase defect detection speed five times from manual detection to automatic detection¹.

How They Drive Hybrid Cloud Solutions:

Intel and Alibaba Cloud established the Apsara Stack Industry Alliance, which focuses on building an ecosystem of hybrid cloud solutions for Alibaba Cloud’s Apsara Stack. Optimized for Intel® Xeon® Scalable processors, the Apsara Stack provides large- and medium-sized businesses with on-premises hybrid cloud services that function the same as hyperscale cloud computing and big data services provided by Alibaba public cloud. This alliance will also enable small- and medium-sized businesses (SMBs) to access technologies, infrastructure and security on par with that of large corporations, while offering them a path to greater levels of automation, self-service capabilities, cost efficiencies and governance.

How They Power eCommerce:

In preparation for the upcoming 11/11 “Singles Day” global shopping festival – which generated in excess of 168.2 billion yuan ($25 billion) in spending during the 2017 celebration – Alibaba plans to trial the next-generation Intel Xeon Scalable processors and upcoming Intel® Optane® DC persistent memory with Alibaba’s Tair workload. This workload is a key value data access and caching storage system developed by Alibaba and broadly deployed in many of Alibaba’s core applications such as Taobao and Tmall. Intel’s compute, memory and storage solutions are optimized for Alibaba’s highly interactive and data-intensive applications. These applications require the infrastructure to keep large amounts of hot accessible data in the memory cache to achieve the desired throughput (queries per second) in order to deliver smooth and responsive user experiences, especially during peak hours of the 11/11 shopping festival.

How They Accelerate the Olympics’ Digital Transformation:

Also announced was a partnership aimed at advancing the digital transformation of the Olympics and delivering volumetric content over the OBS Cloud for the first time at the Olympic Games Tokyo 2020. As worldwide Olympic partners, Intel and Alibaba Cloud, will collaborate with OBS to explore a more efficient and reliable delivery pipeline of immersive media to RHBs worldwide that will improve the fan experience and bring them closer to the action via Intel’s volumetric and virtual reality technologies. This showcases the depth of Intel’s end-to-end capabilities, including the most advanced Intel Xeon Scalable processors powering OBS Cloud, compute power to process high volumes of data, and technology to create and deliver immersive media.

How They Accelerate the Commercialization of Intelligent Roads:

Intel officially became one of Alibaba AliOS’ first strategic partners of the intelligent transportation initiative, aiming to support the construction of intelligent road traffic network and build a digital and intelligent transportation system to realize vehicle-road synergy. Intel and Alibaba will jointly explore v2x usage model with respect to 5G communication and edge computing based on the Intel Network Edge Virtualization Software Development Kit (NEV SDK).

More Context: Intel and Alibaba Cloud Deliver Joint Computing Platform for AI Inference at the Edge

¹Automated product quality data collected by YuMei using JWIPC® model IX7, ruggedized, fan-less edge compute node/industrial PC running an Intel® Core i7 CPU with integrated on die GPU and OpenVINO SDK. 16GB of system memory, connected to a 5MP POE Basler* Camera model acA 1920-40gc. Together these components, along with the Intel developed computer vision and deep learning algorithms, provide YuMei factory workers information on product defects near real-time (within 100 milliseconds). Sample size >100,000 production units collected over 6 months in 2018.

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This article will look at how governments and companies are using AI right now in cities. It will mainly focus on three major categories of applications:

1. Helping officials learn more about how people use cities
2. Improving infrastructure and optimizing the use of these resources
3. Improving public safety in cities

We’ll conclude with some of the future implications of these (above) smart city technologies and trends.

The first step in a city becoming a “smart city” is collecting more and better data. There is an old saying in the world of economic modeling: If you put garbage in, you get garbage out. If an organization doesn’t start with good data, trying to make predictions about how new government policies will work can end up deeply flawed or even counterproductive. Helping cities gather and process data is one place AI is currently being put to use.

AI Learns How People Use Cities

Cities have wealth of possible data sources, such as ticket sales on mass transit, local tax information, police reports, sensors on roads and local weather stations. One huge source of raw data that AI pattern recognition technology is making significantly more manageable is video and photos. NVIDIA predicts that by 2020 there will be 1 billion cameras deployed on government property, infrastructure, and on commercial buildings.

That is far more raw data than could ever be viewed, processed, or analyzed by humans. This is why only a small fraction of cameras are ever actively monitored by people. This is where deep learning comes in. It can count vehicles and pedestrians. It can read license plates and recognize faces. It can track the speed and movements of millions of vehicles to establish patterns. It can process the huge volume of satellite data to count cars in a parking lot or track road use.

To help cities handle this torrent of video NVIDIA launched Metropolis, their intelligent video analytics platform. NVIDIA has over 50 AI city partner companies providing products and applications that use deep learning on GPUs.

Similarly, AT&T launched their Smart Cities framework in 2015 and formed alliances with Cisco, Deloitte, Ericsson, GE, IBM, Intel, and Qualcomm. Earlier this year AT&T announced a deal to be the exclusive reseller of GE Current’s intelligent sensor nodes for connecting cities — a significant deal since GE just announced it will provide San Diego with largest smart city Internet of Things (IoT) sensor platform. There are a huge volume of applications of AI with IoT (some of which we’ve covered here on TechEmergence), but few opportunities match the massive scale of smart cities.

GE is going to start by installing 3,200 CityIQ sensor nodes throughout the city and may expand that to over 6,000. The data can be used to identify parking spots for drivers, help first responders, and identify dangerous intersections.

A straightforward application of machine processing video for cities is license plate recognition (LPR), which is used in numerous ways. For example, since 2014 the City of Galveston has been using the company gtechna’s Pay by Plate number parking system. Instead of traditional analog meters, people can pay by phone and LPR technology verifies who is parked legally.

The system did not require installing any new infrastructure at the city’s historic Seawall, and it reduced deployment costs while preserving the area’s aesthetic. The company claims in the first year the system produced $700,000 in new revenue and the city saw an 80% increase in collection rates.

Similar parking systems are in use in multiple cities and by large private institutions, which are almost mini cities unto themselves. Stanford University uses VIMOC Technologies’ LPR system in its parking lots to quickly check if vehicles parked there have the right permits.

AI Optimizing Infrastructure for Cities

A large amount of existing public infrastructure is underutilized, overused, or used inefficiently due to a lack of real time information among individuals, companies, and government agencies. Drivers don’t know where parking is available, according to analysis by INRIX New York City drivers spend an average of 107 hours per yearlooking for parking. Passengers not knowing how long a bus will take have lower ridership than buses they can track. Cities for the most party don’t know what is the right length for a stop light at every given minute. These are issues companies and government are addressing.

Anyone who has spent 10 minutes driving in circles in a city trying to find a parking space should be aware of this problem. It is a waste of your time, and circling around increases downtown traffic, wasting everyone else’s time. It may seem like a minor inconvenience, but multiply that by millions of people each day in hundreds of cities, and it adds up to a significant waste of net resources.

Well known companies are trying to address this issue from the individual’s perspective. For example, Waze, acquired by Google back in 2013, uses its network of drivers to provide real time data about traffic and accidents to help individuals optimize their routes. Cities are also trying to improve the situation from their end (we covered the specific AI use-case of Waze in our popular “Everyday Examples of AI” article earlier this year).

Smart Parking Garages

VIMOC is using AI to make parking easier in Redwood City. The company installed vehicle detection and reporting in two of the city’s large parking garages. The amount of available parking is displayed outside the garages on large LED signs and shared with an open platform for use by app developers. It provides the immediate benefit letting individuals know where parking is available, and in the long term, the wealth of data collected will allow the city to make planning and pricing decisions.

Adaptive Signal Control Technologies

Adaptive Signal Control Technology allows traffic lights to change their timing based on real time data.  According to the Department of Transportation (DOT), “On average [Adaptive Signal Control Technology] improves travel time by more than 10 percent. In areas with particularly outdated signal timing, improvements can be 50 percent or more.” Given that traffic congestion costs the country $87.2 billion in wasted fuel and productivity (according the the DOT), there is a strong reason why numerous cities and companies are deploying this technology around the country.

The benefits of this technology where it has already been deployed have been promising. For example, San Diego installed 12 Adaptive Traffic Systems along one of its busiest corridors last fall and found they “reduced travel time by as much as 25 percent and decreased the number of vehicle stops by up to 53 percent during rush hour periods.”

Similarly, in 2012 the company Surtrac first deployed intelligent traffic signals at nine intersections in downtown Pittsburgh. It reduced travel times by more than 25% on average and wait times by 40% on average.

As a result Surtrac has been expanded to 50 intersections in the city with more planned. Of course these results come from the technology first being deployed on the busiest roads where it was assumed they would have the biggest impact.

This technology has been widely deployed and proven effective in the last few years in cities like San Antonio, Bellevue and in Los Angeles; but the use of it remains highly limited.

Connected Public Transit Technology

This technology allows buses and trains to communicate with each other and the general public. Letting individuals know when buses or trains are coming and if they are going to run late makes them more useful to individuals.

The Massachusetts Bay Transportation Authority was the first agency to make bus locations and arrival-time predictions available, allowing developers to create tracking apps. Research on the impact of real time information on bus ridership in New York City found that it increased weekday route-level ridership by 1.7%.

AI Improving Public Safety

Smart cities aren’t just about reducing commute times and saving on fuel. The same networks of sensors and cameras are being used to save lives and fight crime.

The same LPR technology used to track parking is used by law enforcement to find stolen cars and track criminals. By 2014 LPR was already being used by an overwhelming majority of local law enforcement.

The same intelligent traffic lights normally used to improve traffic flow are utilized by ambulances and fire trucks to get to the scene of an emergency quicker and more safely.

Shotspotter, a company that automatically locates gunfire based on a sensor network, has its technology embedded in GE intelligent street lights. Last year Shotspotter alerted law enforcement to 74,916 gunfire incidents. In its recent IPO it received aggregate proceeds of approximately $35.4 million.

The growing body of data gathered by cities is being mined to find out which intersections experience accidents, and importantly exactly how these accidents take place, to prevent them in the future. As part of the Vision Zero effort to eliminate all traffic fatalities, cities are turning to big data to plan and prioritize infrastructure projects. For example, Microsoft and DataKind recently partnered with New York, Seattle and New Orleans to use data science to improve safety.

Highly connected cities also provide the ability to give individual hyper localized warnings about possible natural disaster. Do to its unique geography rainfall can vary significantly throughout the city of Seattle. To address this they created RainWatch which combines radar data with a network of rainfall gauges to monitor rainfall with a high degree of resolution. It allows city maintenance workers to more quickly respond to possible problems and provide more accurate flood warning to residents.

Concluding Thoughts on AI for Smart Cities

The grand long term vision of smart cities is full interconnectivity: Self driving cars, trucks, and buses all talking with each other as well as with smart highways, traffic lights, and parking garages. The whole system will working together to move people around with an incredible degree of efficiency and safety. A highly connected system that will save lives, save time, and save fuel. A reality that will be made more possible as the federal government moves towards requiring vehicle-to-vehicle communication build into new vehicles in the coming years.

It is also to provide engineers and city planners with an incredible wealth of data that can be used to promote safety, health, and economic growth. Right now researchers often rely on rough estimates of how people are using most roads and bike paths, but in the future they could have access to a minute by minute breakdown of every block.

The important thing is that we don’t need to see any new technology developed to see massive gains from cities becoming smarter. We have existing technology proven to be capable of improving parking utilization, safety, and significantly improving traffic; it just hasn’t been widely deployed yet.

For example, less than 1 percent of traffic signals in the United States are smart, but the federal government, local governments and many large companies hope to change that.

As a result, the potential for growth for these systems and the companies that make them is significant. It is not surprising, then, that numerous large companies (Siemens, Microsoft, Hitachi, others) have put an increased focus on smart city technology.

Beyond the industries directly providing these services to local governments, the spending on smart cities could impact a range of businesses. Reduced traffic would mean cheaper shipping and technicians being able spend more time at job sites and less time moving between them. Fewer accidents could result in lower insurance costs for everyone.

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David Patterson—University of California professor, Google engineer, and RISC pioneer—says there’s no better time than now to be a computer architect.That’s because Moore’s Law really is over, he says: “We are now a factor of 15 behind where we should be if Moore’s Law were still operative. We are in the post–Moore’s Law era.”

 This means, Patterson told engineers attending the 2018 @Scale Conference held in San Jose last week, that “we’re at the end of the performance scaling that we are used to. When performance doubled every 18 months, people would throw out their desktop computers that were working fine because a friend’s new computer was so much faster.”

But last year, he said, “single program performance only grew 3 percent, so it’s doubling every 20 years. If you are just sitting there waiting for chips to get faster, you are going to have to wait a long time.”

For a computer architect like Patterson, this is actually good news. It’s also good news for innovative software engineers, he pointed out. “Revolutionary new hardware architectures and new software languages, tailored to dealing with specific kinds of computing problems, are just waiting to be developed,” he said. “There are Turing Awards waiting to be picked up if people would just work on these things.”

As an example on the software side, Patterson indicated that rewriting Python into C gets you a 50x speedup in performance. Add in various optimization techniques and the speedup increases dramatically. It wouldn’t be too much of a stretch, he indicated, “to make an improvement of a factor of 1,000 in Python.”

On the hardware front, Patterson thinks domain-specific architectures just run better, saying, “It’s not magic—there are just things we can do.” For example, applications don’t all require that computing be done at the same level of accuracy. For some, he said, you could use lower-precision floating-point arithmetic instead of the commonly used IEEE 754 standard.

The biggest area of opportunity right now for applying such new architectures and languages is machine learning, Patterson said. “If you are a hardware person,” he said, “you want friends who desperately need more computers.” And machine learning is “ravenous for computing, which we just love.”

Today, he said, there’s a vigorous debate surrounding which type of computer architecture is best for machine learning, with many companies placing their bets. Google has its Tensor Processing Unit (TPU), with one core per chip and software-controlled memory instead of caches; Nvidia’s GPU has 80-plus cores; and Microsoft is taking an FPGA approach.

And Intel, he said, “is trying to make all the bets,” marketing traditional CPUs for machine learning, purchasing Altera (the company that provides FPGAs to Microsoft), and buying Nervana, with its specialized neural-network processor (similar in approach to Google’s TPU).

Along with these major companies offering different architectures for machine learning, Patterson says there are at least 45 hardware startups tackling the problem. Ultimately, he said, the market will decide.

“This,” he says, “is a golden age for computer architecture.”

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The new study, led by Luís Amaral of the McCormick School of Engineering, will be published Sept. 17 by the journal Nature Human Behaviour. The findings potentially could be of interest to hiring managers and mental health care providers.

“People have tried to classify personality types since Hippocrates’ time, but previous scientific literature has found that to be nonsense,” said co-author William Revelle, professor of psychology in the Weinberg College of Arts and Sciences.

image: the four newly determined personality types are based on five widely-recognized character traits credit: northwestern university

People have tried to classify personality types since Hippocrates’ time, but previous scientific literature has found that to be nonsense,” said co-author William Revelle, professor of psychology in the Weinberg College of Arts and Sciences.

“Now, these data show there are higher densities of certain personality types,” said Revelle, who specializes in personality measurement, theory and research.

Initially, however, Revelle was skeptical of the study’s premise. The concept of personality types remains controversial in psychology, with hard scientific proof difficult to find. Previous attempts based on small research groups created results that often were not replicable.

“Personality types only existed in self-help literature and did not have a place in scientific journals,” said Amaral, the Erastus Otis Haven Professor of Chemical and Biological Engineering at Northwestern Engineering. “Now, we think this will change because of this study.”

The new research combined an alternative computational approach with data from four questionnaires with more than 1.5 million respondents from around the world obtained from John Johnson’s IPIP-NEO with 120 and 300 items, respectively, the myPersonality project and the BBC Big Personality Test datasets. The questionnaires, developed by the research community over the decades, have between 44 and 300 questions. People voluntarily take the online quizzes attracted by the opportunity to receive feedback about their own personality. These data are now being made available to other researchers for independent analyses.

“The thing that is really, really cool is that a study with a dataset this large would not have been possible before the web,” Amaral said. “Previously, maybe researchers would recruit undergrads on campus, and maybe get a few hundred people. Now, we have all these online resources available, and now data is being shared.”

From those robust datasets, the team plotted the five widely accepted basic personality traits: neuroticism, extraversion, openness, agreeableness and conscientiousness.

After developing new algorithms, four clusters emerged:

Average

Average people are high in neuroticism and extraversion, while low in openness. “I would expect that the typical person would be in this cluster,” said Martin Gerlach, a postdoctoral fellow in Amaral’s lab and the paper’s first author. Females are more likely than males to fall into the Average type.

Reserved

The Reserved type is emotionally stable, but not open or neurotic. They are not particularly extraverted but are somewhat agreeable and conscientious.

Role Models

Role Models score low in neuroticism and high in all the other traits. The likelihood that someone is a role model increases dramatically with age. “These are people who are dependable and open to new ideas,” Amaral said. “These are good people to be in charge of things. In fact, life is easier if you have more dealings with role models.” More women than men are likely to be role models.

Self-Centered

Self-Centered people score very high in extraversion and below average in openness, agreeableness and conscientiousness. “These are people you don’t want to hang out with,” Revelle said. There is a very dramatic decrease in the number of self-centered types as people age, both with women and men.

The group’s first attempt to sort the data used traditional clustering algorithms, but that yielded inaccurate results, Amaral said.

“At first, they came to me with 16 personality types, and there’s enough literature that I’m aware of that says that’s ridiculous,” Revelle said. “I believed there were no types at all.”

He challenged Amaral and Gerlach to refine their data.

“Machine learning and data science are promising but can be seen as a little bit of a religion,” Amaral said. “You still need to test your results. We developed a new method to guide people to solve the clustering problem to test the findings.”

Their algorithm first searched for many clusters using traditional clustering methods, but then winnowed them down by imposing additional constraints. This procedure revealed the four groups they reported.

“The data came back, and they kept coming up with the same four clusters of higher density and at higher densities than you’d expect by chance, and you can show by replication that this is statistically unlikely,” Revelle said.

“I like data, and I believe these results,” he added. “The methodology is the main part of the paper’s contribution to science.”

To be sure the new clusters of types were accurate, the researchers used a notoriously self-centered group — teenaged boys — to validate their information.

“We know teen boys behave in self-centered ways,” Amaral said. “If the data were correct and sifted for demographics, they would they turn out to be the biggest cluster of people.”

Indeed, young males are overrepresented in the Self-Centered group, while females over 15 years old are vastly underrepresented.

Along with serving as a tool that can help mental health service providers assess for personality types with extreme traits, Amaral said the study’s results could be helpful for hiring managers looking to insure a potential candidate is a good fit or for people who are dating and looking for an appropriate partner.

And good news for parents of teenagers everywhere: As people mature, their personality types often shift. For instance, older people tend to be less neurotic yet more conscientious and agreeable than those under 20 years old.

“When we look at large groups of people, it’s clear there are trends, that some people may be changing some of these characteristics over time,” Amaral said. “This could be a subject of future research.”

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The idea came from researchers at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia with expertise in flexible electronics. After talking with colleagues who were cultivating genetically engineered plants in greenhouses, they recognized the need for inexpensive sensors that could be deployed en masse and report on individual plant conditions. Their early offerings include a stretchable sensor for measuring micrometer-level changes in plant growth and a “PlantCopter” temperature and humidity sensor designed to be dropped from a drone and corkscrew its way through the air for a gradual descent.

“When you are deploying PlantCopters, they get stuck strategically to the leaves of the plants because of the design architecture,” says Muhammad Mustafa Hussain, a professor of electrical engineering at KAUST. “Obviously not 100 percent of the PlantCopters will be stuck to plants, but that is fine in the context of their cost.”

The vision of Hussain and his colleagues—including Joanna Nassar, lead author on the study and currently a postdoctoral researcher at the California Institute of Technology in Pasadena—is to bring the Internet of Things to plants through cheap biodegradable materials and low-power Bluetooth wireless communication. Their work was published online in the journal npj Flexible Electronics on 10 September 2019.

Many commercial agricultural monitoring systems often taken the form of extensively-trained computer vision camera systems that monitor plant growth, and baton-shaped sensors stuck in the soil to measure conditions such as temperature or humidity. But these are often ill-equipped to monitor the more minute changes in plant and environmental conditions—or else they are too expensive for many farmers to use on a large scale.

“The plant monitoring systems you can get at Home Depot or Amazon are fairly expensive, and you have to go throughout the field and place them individually without them being able to communicate,” Hussain explains. “And when they get smarter with communication, the price goes up.”

Both KAUST sensors rely on a low-power system that lasted for an average of 151 days while logging data every two seconds during early lab testing. This system consists of a small rechargeable lithium ion battery and a programmable-system-on-chip with 256 kB of internal flash memory to record and store data. Bluetooth chips enable the sensors to transmit their data either to nearby drones or people with smartphones.

First, the researchers created a plant-wearable sensor—made from polymer and thin gold metal film—that has the flexibility to attach in any position on a plant. They tested this stretchable strain sensor on both barley and lucky bamboo plants during trial periods of several hours or days to show that they could detect even the most minute growth changes.

Second, the team created a 3D-printed temperature and humidity sensor that can be dropped from drones in large numbers. This PlantCopter sensor was inspired by dandelion flower seeds and maple seeds that float through the air like miniature helicopters. It’s a design that could ideally allow for widespread dispersion of the sensors with minimal effort, whereas the plant wearable for measuring plant growth would still have to be attached by hand.

An initial demonstration showed that the team could maintain a smartphone Bluetooth connection with the PlantCopter as it was dropped from a height of 50 feet and landed on the ground. But the somewhat bolder vision of the PlantCopter has not yet been tested through a mass airdrop by drone.

Still, the team aims to develop a drone with both sufficient battery life and some autonomous capabilities to perform such duties by April 2019. That timeframe coincides with a plan to conduct field trials in three countries: India, Sri Lanka and Zambia.

All this talk of individual plant sensors may seem like small potatoes. But taken together, such sensors or similar solutions could add up to part of a much larger big data revolution sweeping through agriculture. Farmers will need as much fine-grained data as they can get about what’s happening on the ground in order to boost productivity through a carefully-orchestrated dance of drones, robotic equipment and smart sensors.

“My overarching objective is to collect big data and infuse it with AI so that drones can make real-time decisions like spreading fertilizer and pesticides as needed,” Hussain says. “My vision is if productivity goes up by even one percent, I think that would feed more people.”

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