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Artificial Intelligence

AI Expo: Evolving to emotionally intelligent applications



Speaking at AI Expo in Amsterdam, BPU Holdings CTO Carlos Art Nevarez believes it’s time for machines to become emotionally intelligent.

Machines are becoming increasingly smart thanks to artificial intelligence, but they still remain cold, logical, and lacking emotion. Worse still, they have a bias problem.

“We are teaching the machine to synthetically emulate emotional intelligence to better relate to how you and I feel,” states Nevarez.

“So many exciting applications present themselves to enhance healthcare analytics, market assessment, consumer and voter sentiment, and delivering customised content in the Internet of Things.”

Nevarez recounted a time he was out with his son and they saw a person fall. His son laughed, but – when Nevarez explained the person could be hurt – his son became empathetic. Over time, his son recognised when to show empathy.

AI learns from patterns, and Nevarez believes – much like his son – they can be taught with empathetic values.

“Teaching a machine to feel, is just as important as teaching a machine to think,” says Nevarez. “Or we end up with a world heavily-biased by the engineers that program those AIs.”


The company started with political forecasting. Politics, as we all know, is very much driven by sentiment and ideologies.

In the past couple of years, there’s been some major elections and decisions not many saw coming.

Navarez applied BPU’s emotional computing engine to the US Presidential Elections for a personal project. Based on its analysis, Donald Trump was going to win.

“I wanted to see if our emotional computing engine would come close to predicting the outcome of the election,” recalls Navarez. “After watching the election for about eight weeks, and not really getting a ton of data – because it was just me and I didn’t have the computing resources of the company – I came up with the prediction that Donald Trump was going to win.”

“We called our engineers and said there’s something wrong with our algorithms, it’s predicting Trump is going to win.”

A week later, Donald Trump won.

There is very little polling done via telephone anymore, it’s mostly online. This has increased accuracy as people are more honest online.

“People tend to be more honest when they’re flaming someone on Twitter,” comments Nevarez. “When people ask [in person] ‘How do you feel about this candidate?’ then people want to be nice, they don’t want to hurt someone’s feelings.”

For a customer this time, BPU attempted to predict the Korean elections.

Twitter is less used in South Korea and Nevarez was sceptical it would be accurate. However, yet again, it was able to correctly predict the outcome leading to the election of President Moon Jae-In.

BPU even released its results on the morning of the election, days before traditional pollsters. The worst margin of error was just two percent.

For a final example, BPU showed how it correctly ranked the results of the Nevada US House District Republican Primary election.


The examples prove BPU’s sentiment analysis works. However, it’s understanding an individual’s emotions and helping to alter them (for the better) which could have the greatest impact.

Seth Grimes, Principal Analyst for Alta Plana specialising in natural language processing (NLP), text analytics, and sentiment analysis, states: “Automated emotion understanding — emotion AI — is now a must-have capability for consumer marketing and public-facing campaigns, including electoral campaigns…”


AI will, and is, revolutionising healthcare. It’s also one of the areas where empathy is most needed.

aiMei is an app created by BPU which offers personality tests and mood analysis in a chatbot-like interface. The company made a version of it for the medical industry where a physician can train it for a patient’s needs.

The app could ask whether a person has taken their ibuprofen yet, for example. Being a tablet known for causing stomach irritation when taken on an empty stomach, it could ask whether the individual has eaten yet or not.

A bit later it could ask if the person wants a snack, pre-programmed with those available that day, so a nurse doesn’t have to go and check with each patient.

Finally, the patient may be asked to provide their mood – or how they’re feeling – an hour or so after, to know whether the medication is working or not.

Physicians have reportedly said to BPU that, while they can go monitor things like temperature, they’re unable to keep a record of a patient’s mental wellbeing – but they’d like to.

Salim Hariri, Ph.D., co-director of the Natural Science Foundation and The Center for Autonomic Computing at the University of Arizona, recently stated: “Among many other applications, BPU’s AEI technology shows great potential for healthcare advances in patient emotional and critical assessment.”

The company also has a smartwatch app which can detect when a person’s heart rate is accelerating and look for the potential reason.

By collaborating with a heart surgeon, the smartwatch app is able to accurately predict five minutes before a cardiac arrest is going to occur. This means health professionals can be alerted earlier to begin preparations which could be life-saving.

Outside healthcare, BPU has also produced a personalised news app called Neil which determines a user’s individual emotional reaction to articles in order to serve up more or less of similar coverage.

All of these apps, Nevarez says, is providing the company with a good look at the human emotional genome and helping it to create frameworks that help anyone build empathetic apps.

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Artificial Intelligence

China plans new era of sea power with unmanned AI submarines



China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts.

A report by the South China Post on Sunday revealed Beijing’s plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US.

The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases.

While the expected cost of the submarines has not been disclosed, they’re likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they’ll also need to be resilient enough to be at sea without onboard repairs possible.

The XLUUVs (Extra-Large Unmanned Underwater Vehicles) are much bigger than current underwater vehicles, will be able to dock as any other conventional submarine, and will carry a large amount of weaponry and equipment.

As a last resort, they could be used in automated ‘suicide’ attacks that scuttle the vessel but causes damage to an enemy’s ship that may or not be manned.

“The AI has no soul. It is perfect for this kind of job,” said Lin Yang, Chief Scientist on the project. “[An AI sub] can be instructed to take down a nuclear-powered submarine or other high-value targets. It can even perform a kamikaze strike.”

The AI element of the submarines will need to carry out many tasks including navigating often unpredictable waters, following patrol routes, identifying friendly or hostile ships, and making appropriate decisions.

It’s the decision-making that will cause the most concern as the AI is being designed not to seek input during the course of a mission.

The international norm being promoted by AI researchers is that any weaponised AI system will require human input to ultimately make a decision. Any news that China is following a policy of creating weaponised AIs that do not require human input should be of global concern.

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Artificial Intelligence

AI robots will solve underwater infrastructure damage checks



Robots will be paired with a versatile AI that can quickly adapt to unpredictable conditions when examining underwater infrastructure.

Some of a nation’s most vital infrastructure hides beneath the water. The difficulty in accessing most of it, however, makes important damage checks infrequent.

Sending humans down requires significant training and can take several weeks to recover due to the often extreme depths. There are far more underwater structures than skilled divers to inspect them.

Robots have been designed to carry out some of these dangerous tasks. The problem is until now they’ve lacked the smarts to deal with the unpredictable and rapidly-changing nature of underwater conditions.

Researchers from Stevens Institute of Technology are working on algorithms which enable these underwater robots to check and protect infrastructure.

Their work is led by Brendan Englot, Professor of Mechanical Engineering at Stevens.

“There are so many difficult disturbances pushing the robot around, and there is often very poor visibility, making it hard to give a vehicle underwater the same situational awareness that a person would have just walking around on the ground or being up in the air,” says Englot.

Englot and his team are using reinforcement learning for training algorithms. Rather than use an exact mathematical model, the robot performs actions and observes whether it helps to attain its goal.

Through a case of trial-and-error, the algorithm is updated with the collected data to figure out the best ways to deal with changing underwater conditions. This will enable the robot to successfully manoeuvre and navigate even in previously unmapped areas.

A robot was recently sent on a mission to map a pier in Manhattan.

“We didn’t have a prior model of that pier,” says Englot. “We were able to just send our robot down and it was able to come back and successfully locate itself throughout the whole mission.”

The robots use sonar for data, widely regarded as the most reliable for undersea navigation. It works similar to a dolphin’s echolocation by measuring how long it takes for high-frequency chirps to bounce off nearby structures.

A pitfall with this approach is you’re only going to be able to receive imagery similar to a grayscale medical ultrasound. Englot and his team believe that once a structure has been mapped out, a second pass by the robot could use a camera for a high-resolution image of critical areas.

For now, it’s early days but Englot’s project is an example of how AI is enabling a new era for robotics that improves efficiency while reducing the risks to humans.

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Artificial Intelligence

Technology Trends That Will Dominate 2018



  1. AI permeation. Artificial intelligence (AI), largely manifesting through machine learning algorithms, isn’t just getting better. It isn’t just getting more funding. It’s being incorporated into a more diverse range of applications. Rather than focusing on one goal, like mastering a game or communicating with humans, AI is starting to make an appearance in almost every new platform, app, or device, and that trend is only going to accelerate in 2018. We’re not at techno-pocalypse levels (and AI may never be sophisticated enough for us to reach that point), but by the end of 2018, AI will become even more of a mainstay in all forms of technology.

  2. Digital centralization. Over the past decade, we’ve seen the debut of many different types of devices, including smartphones, tablets, smart TVs, and dozens of other “smart” appliances. We’ve also come to rely on lots of individual apps in our daily lives, including those for navigation to even changing the temperature of our house. Consumers are craving centralization; a convenient way to manage everything from as few devices and central locations as possible. Smart speakers are a good step in the right direction, but 2018 may influence the rise of something even better.

  3. 5G preparation. Though tech timelines rarely play out the way we think, it’s possible that we could have a 5G network in place—with 5G phones—by the end of 2019. 5G internet has the potential to be almost 10 times faster than 4G, making it even better than most home internet services. Accordingly, it has the potential to revolutionize how consumers use internet and how developers think about apps and streaming content. 2018, then, is going to be a year of massive preparation for engineers, developers, and consumers, as they gear up for a new generation of internet.

  4. Data overload. By now, every company in the world has realized the awesome power and commoditization of consumer data, and in 2018, data collection is going to become an even higher priority. With consumers talking to smart speakers throughout their day, and relying on digital devices for most of their daily tasks, companies will soon have access to—and start using—practically unlimited amounts of personal data. This has many implications, including reduced privacy, more personalized ads, and possibly more positive outcomes, such as better predictive algorithms in healthcare.


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