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Daliworks and TrackNet Partner in Deploying Innovative Smart Building Solutions in Asia Pacific

By TheWAY - 5월 23, 2018

Daliworks and TrackNet Partner in Deploying Innovative Smart Building Solutions in Asia Pacific

Daliworks Thing+ IoT Cloud Platform combined with TrackNet’s TrackCentral LoRaWAN network server and LoRa end-to-end connectivity optimizes energy efficiency in buildings and commercial greenhouses.

TrackNet, Inc., a cutting-edge LoRaWAN
Internet of Things (IoT) solutions provider copy. TrackNet homepage

Daliworks, a leading provider of cloud platforms and solutions for the Internet of Things (IoT), and TrackNet, Inc., a cutting-edge LoRaWAN Internet of Things (IoT) solutions provider, announced the deployment of smart building and smart greenhouse solutions in Korea and Japan. The complete end-to-end solution of sensors, network, and cloud IoT platforms optimizes energy efficiency, ensures optimal conditions, and reduces the total cost of management.

The combined solution from Daliworks and TrackNet significantly reduces the cost and improves the return on investment (ROI) for smart building and greenhouse deployments. Daliworks’ Thing+™ application platform provides administration, user roles, billing, alerts, and data analysis. It can also be customized and configured on a per-solution basis. The TrackNet TrackCentral solution provides the most advanced LoRaWAN core network solution, including device customization, device management, firmware updates over the air (FUOTA), and the most advanced network analytics platform to provide end-to-end smart connectivity for IoT deployments and applications.

The use of LoRaWAN creates seamless connectivity easily over an entire campus or building with a minimum number of gateways, dramatically simplifying installation and minimizing infrastructure cost. TrackNet and Daliworks have worked closely to develop customized sensors and end-to-end connectivity to meet the key performance indicators (KPIs) that building owners have for these systems.

Access to full-building control, monitoring, and data collection has traditionally required long and costly project-based development, creating a barrier for all but the largest buildings to “get smart.” Through this solution, the cost and expertise required is reduced while delivering the same quality data.

“Daliworks’ market knowledge and powerful cloud platform makes it the ideal partner for TrackNet to develop large-scale deployments in Korea,” says Hardy Schmidbauer, CEO of TrackNet. “We believe smart building is one of the fastest growing market segments in IoT due to its very high ROI.”

“What we’ve built together with TrackNet is nothing less than a complete disruption of this market. We enable any building, large or small, old or new, to immediately skip a generation of costly capital-intensive equipment,” remarks Soonho Lee, CEO of Daliworks.

About TrackNet

TrackNet provides highly scalable LoRaWAN IoT solutions for consumers and industry, focusing on ease of use and unparalleled scalability to enable a new era of exponentially growing LPWAN deployments for a sustainable world. TrackNet has developed the most powerful core LoRaWAN network server and end-to-end smart connectivity for targeted LPWAN and IoT applications. The company is a contributing member of the LoRa Alliance and the TrackNet team has been instrumental in specifying, building, and establishing LoRaWAN and the LoRa Alliance for more than five years. It has been brought to life by former Semtech and IBM employees with decades of cumulated real-world experience in embedded and RF systems, wireless networks, network management, end-to-end security, and solutions development for the whole spectrum from small-scale private networks to carrier-grade deployments. TrackNet has offices in Switzerland and California. More information is at www.tracknet.io.

About Daliworks

Daliworks provides a cloud-based IOT platform containing the core functionality required for businesses to create and customize end-to-end IOT solutions. Focused on supporting quick-to-market IOT development, this platform, Thing+, is the leading platform for IOT application and solution development in South Korea. The platform enables most forms of dashboard-based data visualization, automated rules and alarms, and data analysis. Data processing and handling is provided by the Thing+ cloud, able to be run on any cloud system, or as an “on-premise” solution that can be run on a local data center or server. Administration, user roles, billing, and data analysis are all part of the core platform and can be customized and configured on a per-solution basis. Daliworks’ team is primarily composed of experts in the fields of M2M and telecommunications and works directly with some of the largest companies in Korea. For more information, please visit www.daliworks.net.

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12 Ways Artificial Intelligence Can Make You a Healthier Man

By TheWAY - 5월 22, 2018

12 Ways Artificial Intelligence Can Make You a Healthier Man

Artificial intelligence is medicine's future, but certain aspects are available now.

When Greg Corrado, Ph.D., an artificial intelligence researcher, took the stage at the TedMed Conference last year, he was frank.

"Doctors who partner with artificial intelligence as a decision-making aid will see their healing powers expand more than they have in the past 100 years," he told the audience of medical professionals. Corrado is a principal scientist at Google AI and an expert in machine learning. "To practice medicine today," he continued, "is to weather an information hurricane...AI [and machine learning] is our best opportunity to tame the data beast and actually scale care to meet demand."

Sound like sci-fi? Not to the hospitals, researchers, and med schools working with Microsoft, Apple, IBM, Google Intel, and GE. Companies are creating algorithms to sort medical records, determine treatments, diagnose sepsis in as little as 12 hours, and even predict who will skip their next doctor's appointment. Cloud-based, deep-learning technology is also being used to review MRIs to assess bloodflow and scan for heart disease faster and more accurately than even the best radiologists can.

Although the field is still in its infancy, here are 12 innovative AI-based technologies you can tap today to improve your health.

THE VOORHES

Sensely

Admit it—you've always wanted a private-duty nurse. Well, next time you have surgery, an interactive, virtual health assistant named Molly may follow up with you at home. The avatar can check symptoms, pull info from medical devices and wearables, and book follow-up appointments. No need to type—you speak to Molly, and all your info is automatically uploaded to your medical file for your doctor to review.

Best for:

People who are frustrated by the hassles of conventional follow-up care or who need to closely monitor a chronic disease

Cost:

Free in connection with your doctor/hospital

Woebot

Wary of therapy? Not into talking about your mental health? This chatbot, created by Stanford clinical research psychologist Alison Darcy, Ph.D., uses brief daily conversations, mood graphing, uplifting videos, and word games via Facebook's Messenger to monitor your mood and help you learn about yourself. Over time, Woebot learns what you respond to best, and its reactions become more specific. In a 2017 study, users saw significant reductions in depression and anxiety symptoms after two weeks and an average of 12 sessions using Woebot.

Best for:

The therapy-averse

Cost:

$39/month

PATRICK LEGER

AiCure

Missed doses of prescription meds can interfere with recovery and even be lifethreatening. AiCure uses AI and a smartphone camera to visually confirm that the pill was taken. After checking the patient’s identity through facial recognition, this clinically backed app verifies the medication type (based on its shape and color) and ingestion, and then sends confirmation to a doctor. It also sends daily medication reminders.

Best for:

The incredibly busy, the hopelessly absent-minded, and especially remote caretakers of aging parents

Cost:

Free in connection with your doctor/hospital

Semantic Scholar

When you’re given a life-altering diagnosis and you need to educate yourself on treatment options, forget Google. This AIpowered search engine checks a database of over 40 million peer reviewed studies, pulls the most cited and relevant ones, and finds connections based on effectiveness. Its longterm goal is using machine intelligence to connect the dots between studies and identify cures that might otherwise be missed.

Best for:

Anyone needing trustworthy medical information drawn from the latest science

Cost:

Free

ARTAS Robotic Hair Restoration

Existing hair transplant methods can be painstaking: Hairs are manually harvested from a donor spot on your head and then implanted in bald or thinning areas. This AIpowered robot assists surgeons in selecting the best hairs for harvesting and then transplanting them, which is said to encourage faster recovery and a more natural look.

Best for:

Balding men with money

Cost:

Dependent on number of grafts transplanted

THE VOORHES

HeartFlow

Instead of the dyes and catheters involved in an angiogram, doctors are diagnosing heart disease with this non-invasive technology that uses CT scans, computer generated bloodflow, and a deep learning algorithm to produce a 3D map of the heart. The detailed analyses of blockages and bloodflow have been shown to reduce the number of unnecessary invasive angiograms by more than 80 percent, potentially reducing health care costs by more than 25 percent. It also comes with 86 percent diagnostic accuracy (versus 65 percent for coronary CT angio graphy). It’s available in some 40 hospitals in the United States, with many more expected in the future.

Best for:

Anyone who's in need of a comprehensive heart disease diagnosis

Cost:

Dependent upon insurance coverage (reimbursed by many private insurers)

Butterfly iQ

Doctors are often delayed in making diagnoses because they don’t have immediate access to ultrasound machines. If you’re in pain from, say, possible kidney stones, that wait can be excruciating. The Butterfly iQ is a pocketsized ultrasound scanner that connects to an iPhone, with AI software that helps the doctor interpret the images quickly. It’'s cleared by the FDA for 13 different indications. While it’s not available to consumers, it will appear in ERs in 2018.

Best for:

Any health care provider who needs an ultrasound, stat

Cost:

$2,000

Watson for Oncology

Physicians dealing with cancer cases can sometimes take weeks to work up a treatment plan. IBM’s Watson for Oncology draws on training from experts at Memorial Sloan Kettering Cancer Center as well as a plethora of medical literature —including more than 300 medical journals and 200 textbooks, and nearly 15 million pages of text— to tailor a treatment plan based on the patient’s symptoms, genetics, and history. And it does it in minutes.

Best for:

Cancer patients who want the real-world sensibility of a doctor and an AI-based opinion

Cost:

Varies based on provider

Lumo Run

Simple: Clip this sensor to your shorts, and the MotionScience Platform analyzes biomechanic feedback (cadence, braking, bounce, pelvic rotation, pelvic drop) to deliver realtime tips on your running form. It also suggests pre and postrun exercises to address weak spots. Another product, Lumo Lift (worn on the collarbone) works similarly for posture.

Best for:

Recreational and competitive runners who want to improve their form and avoid injury

Cost:

$100 ($80 for Lift)

THE VOORHES

PatientsLikeMe

After a diagnosis, share your disease experience, symptoms, and treatment info on PatientsLikeMe—, the world’s largest patient network, —to gain insight from people with the same condition. With more than 600,000 members and 2,800 conditions, the platform now uses AI in its DigitalMe program, which creates a more complete version of each patient by analyzing their genes and biomarkers, tracing the disease’'s progression, and studying the factors affecting it.

Best for:

Those seeking insight via crowdsourcing, as well as research advocates, especially if it’s a disease that isn’t well understood, like ALS.

Cost:

Free

Colgate Connect E1

This toothbrush uses 3D motion sensors, an accelerometer, a gyroscope, and a magnetometer to track its exact position and orientation, giving you real-time feedback via your phone as you brush. AI sifts through the data to send weekly coaching tips.

Best for:

Budding actors and the cavity prone

Cost:

$99

Noom

This app claims to have changed 45 million lives, with 78 percent of users losing weight and keeping it off nine months later. By using AI and human “goal specialists,” it creates custom nutrition/exercise courses. Noom is the first program of its kind to be recognized by the CDC for diabetes prevention.

Best for:

People who need personal coaching and are organized enough to input their data consistently

Cost:

Plans start at $32/month after a 14-day trial

SOURCE: https://www.menshealth.com/health/a20665885/artificial-intelligence-health-care/

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TARGETING THE RIGHT CUSTOMERS USING ARTIFICIAL INTELLIGENCE

By TheWAY - 5월 21, 2018

TARGETING THE RIGHT CUSTOMERS USING ARTIFICIAL INTELLIGENCE

Guest post from James Warner of NexSoftSys

Isn’t it an all-time wish for every company to see through the minds of their customers and find out their preferences? Only if there were a realistic way to do that!
There is actually a way to cater to the needs of the customers by predicting what they want. Artificial Intelligence, for all the right reasons, is being employed by many companies to find out exactly what their potential customers are looking for and give it to them at the right time. On the other hand, AI is also used by companies to create a whole new set of target customers that sometimes they never knew existed!

Take the instance of Asafi Jacobi who runs a Harley-Davidson dealership in New York. He was selling one or two motorcycles per week. He hired an AI firm, Adgorithms, and made use of Albert, an AI-driven marketing platform used by Adgorithms. In one weekend, Albert helped him sell 15 motorcycles!

Jacobi had estimated that 2% of the city’s population was his potential buyers. But Albert revealed that his market was much bigger than he estimated and found him buyers that Jacobi never knew they existed in the first place!

Personalization – Just what a customer wants to see!

Before the introduction of AI, companies used to broadly categorize their customers and made bulk decisions about a huge section of target customers. Finding customers out of this scope was not on cards and companies stuck with this set of potential customers all through the way.

Now AI is changing the way companies sees the customers. Each and every potential customer is analyzed by AI. AI records and analyzed every action made by a user – those who visited the website, the products they browsed, the items they added to the cart and removed, the items left in the cart, a call-to-action followed and left in midway, customers who signed up but never visited and so on. AI analyzes these behaviors of each and every customer and finds out the reasons behind the customers for stopping in their pursuit.

Personalization is, as everyone knows, the key to great marketing strategy. Personalizing a segment of customers is what marketers normally do. But AI goes a step beyond that and comes with the content that could focus on converting a particular lead to sales. It personalizes the content in line with what the customer wants to see.

Data and metrics – What AI need

25% of Millennials spend more than 5 hours a day on their smartphones and more than 50% spend at least 3 hours a day on their smartphones. According to a survey by comScore, an average American above the age of 18 years spends 2 hours, 51 minutes on their smartphones every day.

As people are spending a lot of time online, they leave a track of data that gives hint to their characteristics and behavior. These data will be essential in predicting the next move a customer will make on their journey to sales. Also, it will be quite useful in creating look-alike audience who will then be targeted and retargeted for conversions.

Collecting and analyzing data is the backbone of the AI that propels it to function properly. AI employs techniques like mining data from social media to collecting existing historical data from Customer Relationship Management (CRM) to identify high-value targets and new leads.

AI analyzes the engagement patterns and combs through millions of user interactions and actions to create human-like interactions without any involvement of humans! That’s the beauty of AI.

Targeting the right customer with the right content at the right moment through the right channel

Do you know that an average customer is exposed to 10,000 brand messages a day?
So how can you make your message stand out from among the crowd?
Users exhibit so many behaviors online that it takes a great deal of effort to evaluate the user, find if they come up under the category of potential customers and worth spending the time. AI takes all these factors into consideration and much more to assess the user and their preferences to target them.

By presenting your message or advertisement to the customer at their exact time of need, the prospects of conversions are increased. The next step needed in convincing the customer to take an action is possible by crafting a personalized content that hits the right chord with the customer.

Marketers generally have pre-defined channels allocated for targeting based on specific criteria. AI tests the options and makes use of the data to come up with its own model of choosing the right channel to target with.

But a marketer cannot be at the right place at the right time all the time. Hence AI helps to target potential customers at the right moment and present them with the content with a human touch that appeals to their preferences. A study by Adlucent also concedes the fact that 71% of users prefer seeing ads that are tailored to their interests and shopping habits.

Hence more companies have started using AI to come up with innovative ideas to target new users and employ advanced retargeting techniques to drive more sales from new customers.

The difference between a success and a failure may be as simple as a single word or a font size. AI correlates the information with the customers and bridges the gap between the data and the related actions to get what the marketers want.

SOURCE: https://irishtechnews.ie/targeting-the-right-customers-using-artificial-intelligence/

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To Build Truly Intelligent Machines, Teach Them Cause and Effect

By TheWAY - 5월 16, 2018

To Build Truly Intelligent Machines, Teach Them Cause and Effect

Judea Pearl, a pioneering figure in artificial intelligence, argues that AI has been stuck in a decades-long rut. His prescription for progress? Teach machines to understand the question why.

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his latest book, “The Book of Why: The New Science of Cause and Effect,” he argues that artificial intelligence has been handicapped by an incomplete understanding of what intelligence really is.

Three decades ago, a prime challenge in artificial intelligence research was to program machines to associate a potential cause to a set of observable conditions. Pearl figured out how to do that using a scheme called Bayesian networks. Bayesian networks made it practical for machines to say that, given a patient who returned from Africa with a fever and body aches, the most likely explanation was malaria. In 2011 Pearl won the Turing Award, computer science’s highest honor, in large part for this work.

But as Pearl sees it, the field of AI got mired in probabilistic associations. These days, headlines tout the latest breakthroughs in machine learning and neural networks. We read about computers that can master ancient games and drive cars. Pearl is underwhelmed. As he sees it, the state of the art in artificial intelligence today is merely a souped-up version of what machines could already do a generation ago: find hidden regularities in a large set of data. “All the impressive achievements of deep learning amount to just curve fitting,” he said recently.

In his new book, Pearl, now 81, elaborates a vision for how truly intelligent machines would think. The key, he argues, is to replace reasoning by association with causal reasoning. Instead of the mere ability to correlate fever and malaria, machines need the capacity to reason that malaria causes fever. Once this kind of causal framework is in place, it becomes possible for machines to ask counterfactual questions — to inquire how the causal relationships would change given some kind of intervention — which Pearl views as the cornerstone of scientific thought. Pearl also proposes a formal language in which to make this kind of thinking possible — a 21st-century version of the Bayesian framework that allowed machines to think probabilistically.

Pearl expects that causal reasoning could provide machines with human-level intelligence. They’d be able to communicate with humans more effectively and even, he explains, achieve status as moral entities with a capacity for free will — and for evil. Quanta Magazine sat down with Pearl at a recent conference in San Diego and later held a follow-up interview with him by phone. An edited and condensed version of those conversations follows.

Why is your new book called “The Book of Why”?

It means to be a summary of the work I’ve been doing the past 25 years about cause and effect, what it means in one’s life, its applications, and how we go about coming up with answers to questions that are inherently causal. Oddly, those questions have been abandoned by science. So I’m here to make up for the neglect of science.

Photo of Judea Pearl's book "The Book of Why"

That’s a dramatic thing to say, that science has abandoned cause and effect. Isn’t that exactly what all of science is about?

Of course, but you cannot see this noble aspiration in scientific equations. The language of algebra is symmetric: If X tells us about Y,then Y tells us about X. I’m talking about deterministic relationships. There’s no way to write in mathematics a simple fact — for example, that the upcoming storm causes the barometer to go down, and not the other way around.

Mathematics has not developed the asymmetric language required to capture our understanding that if X causes Y that does not mean that Ycauses X. It sounds like a terrible thing to say against science, I know. If I were to say it to my mother, she’d slap me.

But science is more forgiving: Seeing that we lack a calculus for asymmetrical relations, science encourages us to create one. And this is where mathematics comes in. It turned out to be a great thrill for me to see that a simple calculus of causation solves problems that the greatest statisticians of our time deemed to be ill-defined or unsolvable. And all this with the ease and fun of finding a proof in high-school geometry.

You made your name in AI a few decades ago by teaching machines how to reason probabilistically. Explain what was going on in AI at the time.

The problems that emerged in the early 1980s were of a predictive or diagnostic nature. A doctor looks at a bunch of symptoms from a patient and wants to come up with the probability that the patient has malaria or some other disease. We wanted automatic systems, expert systems, to be able to replace the professional — whether a doctor, or an explorer for minerals, or some other kind of paid expert. So at that point I came up with the idea of doing it probabilistically.

Unfortunately, standard probability calculations required exponential space and exponential time. I came up with a scheme called Bayesian networks that required polynomial time and was also quite transparent.

Yet in your new book you describe yourself as an apostate in the AI community today. In what sense?

In the sense that as soon as we developed tools that enabled machines to reason with uncertainty, I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem. All they want is to predict well and to diagnose well.

I can give you an example. All the machine-learning work that we see today is conducted in diagnostic mode — say, labeling objects as “cat” or “tiger.” They don’t care about intervention; they just want to recognize an object and to predict how it’s going to evolve in time.

I felt an apostate when I developed powerful tools for prediction and diagnosis knowing already that this is merely the tip of human intelligence. If we want machines to reason about interventions (“What if we ban cigarettes?”) and introspection (“What if I had finished high school?”), we must invoke causal models. Associations are not enough — and this is a mathematical fact, not opinion.

People are excited about the possibilities for AI. You’re not?

As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.

The way you talk about curve fitting, it sounds like you’re not very impressed with machine learning.

No, I’m very impressed, because we did not expect that so many problems could be solved by pure curve fitting. It turns out they can. But I’m asking about the future — what next? Can you have a robot scientist that would plan an experiment and find new answers to pending scientific questions? That’s the next step. We also want to conduct some communication with a machine that is meaningful, and meaningful means matching our intuition. If you deprive the robot of your intuition about cause and effect, you’re never going to communicate meaningfully. Robots could not say “I should have done better,” as you and I do. And we thus lose an important channel of communication.

What are the prospects for having machines that share our intuition about cause and effect?

We have to equip machines with a model of the environment. If a machine does not have a model of reality, you cannot expect the machine to behave intelligently in that reality. The first step, one that will take place in maybe 10 years, is that conceptual models of reality will be programmed by humans.

The next step will be that machines will postulate such models on their own and will verify and refine them based on empirical evidence. That is what happened to science; we started with a geocentric model, with circles and epicycles, and ended up with a heliocentric model with its ellipses.

Robots, too, will communicate with each other and will translate this hypothetical world, this wild world, of metaphorical models.

When you share these ideas with people working in AI today, how do they react?

AI is currently split. First, there are those who are intoxicated by the success of machine learning and deep learning and neural nets. They don’t understand what I’m talking about. They want to continue to fit curves. But when you talk to people who have done any work in AI outside statistical learning, they get it immediately. I have read several papers written in the past two months about the limitations of machine learning.

Are you suggesting there’s a trend developing away from machine learning?

Not a trend, but a serious soul-searching effort that involves asking: Where are we going? What’s the next step?

That was the last thing I wanted to ask you.

I’m glad you didn’t ask me about free will.

In that case, what do you think about free will?

We’re going to have robots with free will, absolutely. We have to understand how to program them and what we gain out of it. For some reason, evolution has found this sensation of free will to be computationally desirable.

In what way?

You have the sensation of free will; evolution has equipped us with this sensation. Evidently, it serves some computational function.

Will it be obvious when robots have free will?

I think the first evidence will be if robots start communicating with each other counterfactually, like “You should have done better.” If a team of robots playing soccer starts to communicate in this language, then we’ll know that they have a sensation of free will. “You should have passed me the ball — I was waiting for you and you didn’t!” “You should have” means you could have controlled whatever urges made you do what you did, and you didn’t. So the first sign will be communication; the next will be better soccer.

Now that you’ve brought up free will, I guess I should ask you about the capacity for evil, which we generally think of as being contingent upon an ability to make choices. What is evil?

It’s the belief that your greed or grievance supersedes all standard norms of society. For example, a person has something akin to a software module that says “You are hungry, therefore you have permission to act to satisfy your greed or grievance.” But you have other software modules that instruct you to follow the standard laws of society. One of them is called compassion. When you elevate your grievance above those universal norms of society, that’s evil.

So how will we know when AI is capable of committing evil?

When it is obvious for us that there are software components that the robot ignores, consistently ignores. When it appears that the robot follows the advice of some software components and not others, when the robot ignores the advice of other components that are maintaining norms of behavior that have been programmed into them or are expected to be there on the basis of past learning. And the robot stops following them.

SOURCE: https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/

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The Metropolitan Police Service Selects Northgate Public Services as the Technology Partner for Its MiPS Digital Transformation

By TheWAY - 5월 10, 2018

The Metropolitan Police Service Selects Northgate Public Services as the Technology Partner for Its MiPS Digital Transformation

LONDON & TOKYO-- NEC Corporation (NEC)(TOKYO:6701) and Northgate Public Services (NPS), a wholly owned NEC subsidiary, today announced that NPS will provide the Metropolitan Police Service (MPS), the UK’s largest police service, with the technology for its Met Integrated Policing Solution (MiPS) project, which will significantly transform the way officers prevent crime and support victims.

The contract will see MPS using NPS’ CONNECT technology to improve how information is shared and managed, giving officers instant access to key information on investigations, suspects and victims anytime and on any device. Putting real time intelligence into the hands of officers dealing with incidents will provide valuable support when they most need it and support effective deployment of resources.

CONNECT will manage the MPS information that drives policing activity across London and will cover intelligence, investigations, custody and prosecution.

The deal positions NPS as the market leader for policing IT solutions in the UK and will mean that 50% of police officers in England will soon be using its CONNECT technology.

Deputy Assistant Commissioner Duncan Ball, leading the project for the MPS, said: “I am very pleased a contract has been signed with Northgate Public Services to deliver an integrated IT system for the Met. The technology will transform and improve the way information is shared and managed and greatly support investigations and our work in keeping the public safe and bringing offenders to justice.”

Masakazu Yamashina, executive vice president, NEC Corporation said: “We are proud to see our synergies with NPS result in this kind of valuable contribution to the operations and efficiency of the MPS. Going forward, we aim to capitalize on this experience in order to drive the expansion of our Safer Cities business and the enhancement of safety and security for communities throughout the world.”

Ian Blackhurst, executive director safety and health at Northgate Public Services commented: “The Met is focused on modernising its services and making the streets of London safer. Integrating our CONNECT technology will enable officers to instantly prioritise resource by using accurate information on cases and evidence giving them the confidence to make informed decisions on the frontline.”

CONNECT enables officers to refocus resources to where they’re needed most. The integrated system eliminates the need for officers to return to the police station to update records and access key intelligence, helping to achieve a truly mobilised police force. Officers will have the ability to perform duties remotely, giving them greater visibility on the streets and more time to spend with the public.

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Why is your new book called “The Book of Why”?

That’s a dramatic thing to say, that science has abandoned cause and effect. Isn’t that exactly what all of science is about?

You made your name in AI a few decades ago by teaching machines how to reason probabilistically. Explain what was going on in AI at the time.

Yet in your new book you describe yourself as an apostate in the AI community today. In what sense?

People are excited about the possibilities for AI. You’re not?

The way you talk about curve fitting, it sounds like you’re not very impressed with machine learning.

What are the prospects for having machines that share our intuition about cause and effect?

When you share these ideas with people working in AI today, how do they react?

Are you suggesting there’s a trend developing away from machine learning?

Not a trend, but a serious soul-searching effort that involves asking: Where are we going? What’s the next step?

That was the last thing I wanted to ask you.

I’m glad you didn’t ask me about free will.

In that case, what do you think about free will?

We’re going to have robots with free will, absolutely. We have to understand how to program them and what we gain out of it. For some reason, evolution has found this sensation of free will to be computationally desirable.

In what way?

You have the sensation of free will; evolution has equipped us with this sensation. Evidently, it serves some computational function.

Will it be obvious when robots have free will?

Now that you’ve brought up free will, I guess I should ask you about the capacity for evil, which we generally think of as being contingent upon an ability to make choices. What is evil?

So how will we know when AI is capable of committing evil?

The Metropolitan Police Service Selects Northgate Public Services as the Technology Partner for Its MiPS Digital Transformation

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