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‘Is There a Dark Side to Artificial Intelligence and Neuroscience?’ Cartoon

cartoon of a scary-looking spider robot with three scientists running away from it
Artificial intelligence & neuroscience seem to have dark secrets lurking like a robot in the shadows. Are we building obscure, high-risk thinking machines?

Artificial Neuroscience – The Dark Secret Scientists Are Struggling to Understand

How do you teach a machine to learn? What constitutes artificial intelligence? Do machines have a threshold? These are the questions staring in the face of data scientists and researchers about neuroscience and artificial intelligence. The scary part of artificial intelligence is the fact that computers are able to decide for themselves in ways humans can no longer understand. Even the engineers who built the computers cannot fully explain their behavior.

During the 1990s, IBM tackled the problem of AI by providing a brute force solution to winning chess against the World Chess Champion Gary Kasparov. Deep Blue was programmed to look for winning lines and then following numerous moves to find one which would win the game. The programmers knew how the computer won chess because they created chess algorithms, and tweaked the system parameters, and heavily upgraded to win the rematch with Kasparov by a score of 3.5-2.5.

Evolution to Google DeepMind

Two years ago, Google’s DeepMind won an even more impressive AI demonstration when it defeated the reigning Go world champion with a score of 4-1. This was achieved by teaching DeepMind’s AI how to play Go and let it play against itself learning the game along the way. It also found a way to determine whether it is winning at any point in time during the game. This is not a trivial achievement, as Go players are rarely able to determine who is winning during the course of a game. The two decades between Deep Blue and DeepMind show the vast difference in power and direction that AI is taking.

The current engagement of AI is all about teaching a computer how to learn. This is the fundamental thing about neural networks, and how this emulation of natural systems is about to lead to all kinds of automation and new research findings.

Closer Look at Neural Networks

Artificial neural networks are systems of computing that imitate the functions of biological neural networks. The system progressively improves performance on tasks, equivalent to learning on humans, by considering examples without the need for task-specific programming. Neural networks are a powerful approach to machine learning by allowing computers to understand images, translate sentences, recognize speech, and do much more.

In recognizing images, the artificial neural networks will learn to identify images containing dogs by analyzing samples of images that have been labeled as a dog or no dog. The machine will identify dogs in other images by using the results. The machine will identify or recognize the dog without any prior knowledge of a dog. The machine will evolve its own set of characteristics about dogs from the learning materials that it processed.

The artificial neurons are organized in layers, with different layers performing different kinds of decisions on their inputs. A signal will travel the input, which is the first layer, to the last layer, the output, after traveling through the layers several times.

The main goal of the artificial neural network is to solve problems in the same manner that a human brain would. However, artificial neural network deviated from biology when it shifted its focus to the matching of specific tasks. The network has been used to perform several tasks, such as speech recognition, computer vision, social network filtering, machine translation, playing games, and making a medical diagnosis.

The Growing Problem with Neural Networks

People are beginning to see the growing problems with neural networks. Nobody really understands how the most advanced machines perform their tasks. For instance, nobody can predict how an autonomous vehicle will respond to emergency situations, which means that once released onto the streets, nobody knows what the outcome will be. 

“When there’s a lot of interest and funding around something, there are also people who are abusing it. I find it unsettling that some people are selling AI even before we make it, and are pretending to know what [problem it will solve),” according to Tomas Mikolov, Research Scientist Facebook AI.

Understanding how the neural networks function will be very difficult. Normally, when a network is created, there is always the understanding of how it will arrive at a specific decision. Research on how the neural network functions have been mainly focused on detecting which neuron in the network has been activated. Even if the researcher found out that a particular neuron fired signals multiple times, it will not give a clear picture of what is going on in the entire network.

Researchers at Google have spent considerable time studying how the neural network functions in different areas. They have done a lot of work, especially in the area of feature visualization, using remarkable research methods and tools. Google researchers, in fact, have published a paper titled “The Building Blocks of Interpretability,” which proposes new ideas on understanding how deep neural networks arrive at their decisions.

Google’s research does not aim to find out the different interpretability techniques used by the neural networks but to create composable building blocks that may be used in larger models that will shed light on understanding the behavior of neural networks.

Is Humanity Becoming “the Matrix?”

Feature visualization, as a function of the neural network, will be easier to understand, but the idea how the neural network performs this particular function will not apply to the understanding of how the network arrives at its overall decision. Attribution can better explain the relationship between neurons but cannot be used to explain the decision that each individual neuron makes. By combining the building blocks, Google researchers have created a model that can explain what the neural network detects, and answers the question of how the network assembles the individual pieces to arrive at a decision, and also why the decision was made.

“Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method,” says Tommi Jaakkola, Professor at MIT.

The main innovation of the Google model for interpretability is its analysis of the decisions made by the various components of a neural network at the different levels: decisions of the individual neurons, decisions of the connected groups of neurons, and decisions of the complete layers. Google research also used a new research technique – matrix factorization – to analyze the impact that the arbitrary groups of neurons made in the final decisions.

The best way to understand Google’s interpretability blocks is to look at a model that can detect the insights of the decisions arrived at by a network of neurons at the different levels of abstractions from the basic computation to the final decision.

The Dark Secret at the Heart of AI

At its core, the darkest secret of artificial intelligence is that the most advanced algorithms of artificial neural networks are beyond the realm of human understanding.

Nvidia, famous as a chipmaker, released an experimental vehicle onto the streets of Monmouth County in New Jersey. The experimental car looked the same as the autonomous cars of Tesla, Google, and General Motors, but was different because of the use of sophisticated artificial intelligence. While the typical autonomous vehicle followed the instructions provided by the programmer or engineer, the Nvidia car relied solely on an algorithm that it learned itself by watching how humans drive a car.

Making a car learn how to drive by itself in an extraordinary achievement but it is quite worrisome because it is not clear how the car makes its own decisions. The multiple sensors in the vehicle feed information straight into the car’s network of neurons that process the data and deliver the commands to operate the steering wheel and other systems that will keep the car driving on the road at varying road conditions. The network of artificial neurons was able to match the responses expected of a human driver in similar conditions.

There is simply no way to design a system that will explain why it did something that was not expected,” John R. Miles

The worry lies in the possibility that at any given moment, the car may not respond as expected. If the car will refuse to drive forward after sitting on a red light, or it will crash into something, no one can explain or understand how it happened. The network of artificial neurons is very complicated that even the engineers who designed the network may not have the explanation of why the unexpected response happened.

In an autonomous car that functions by following a specific program, going over the program and correcting the flaw may correct a malfunction. But in a vehicle that makes a decision by itself, there is no program to review and correct.

The Growing Mystery of NeuroScience

The mystery of how a vehicle of this nature functions points to the main problem with artificial intelligence. Artificial intelligence technology, deep learning, is a powerful tool for solving problems. It can recognize images, voices, and translate languages. When properly utilized, this deep learning technique can be utilized in making crucial trade decisions and diagnose diseases. However, it is a highly risky proposition, which should not be adopted yet unless we can come up with techniques to make deep learning more understandable to the creators and accountable to the users. Unless we get a clear understanding of how the system works and predict its outcome, the inevitability of failure is always present and no one can correct it.

“We can build these models but we don’t know how they work,” according to Joel Dudley Mount Sinai

With all the achievements attributed to the Nvidia car, it is still in the experimental stage. It will remain experimental until the creators can provide an explanation for the decisions that the automated systems make.

Are humans crossing the threshold of artificial intelligence? Is society building machines whose thought processes we cannot explain? More importantly, are people ready for the age of machines that think differently than humans do?  These questions and more should be answered in the future.

EV Charging Vendors To Disrupt The Market Sales By 2025

Anyos Jedik is a name synonymous with the growing industry of electric vehicles (EV). This Hungarian national invented the very first technology-powered machine in 1828. Since then, different companies and inventors have continuously supported the evolutions of EVs.

The strong demands for EVs has led to the sudden progress of one of its elements, the electric vehicle charging.

According to a recent report from the Grand View Research Incorporated, the market value of electric vehicle charging infrastructure will proliferate to as high as $45.59 billion by 2025.

None of these would be possible without the help of the EV charging vendors. The following companies are leading the way of electric vehicle charging infrastructure in terms of the estimated revenues, and the type of chargers and connectors they offer to the clients:


AeroVironment Incorporated – The California-based firm has been serving the EV industry since 1971. AeroVironment Incorporated develops a range of electronic vehicle charging solutions to top global automakers such as Chevrolet, Ford, Nissan, KIA, Volvo, FIAT, BMW, Mitsubishi, and Hyundai. Wahid Nawabi is the President and CEO of the well-known firm.

AeroVironment is known for producing high-quality drones or UAVs, but when it comes to the EV charging market, it is expected that the company will have a projected revenue of $10 million. The company is creating a name for itself because of its TurboCord Dual Plug-in EV Charger, which critics say is durable and faster than other products.

ChargePoint Incorporated – ChargePoint is a rising startup company that is located in Campbell, California. Headed by Pasquale Romano, the company owns and controls a series of electric vehicle charging network that lets people charge their vehicles.

It has an estimated revenue of $15 million. The Home 25 ft. Cord Electric Vehicle Charger of the startup company has good physical features such as a universal connector, status light, and a WiFi connectivity. Users can also install the EV charger either inside their houses or outside.

ClipperCreek Incorporated – Just like ChargePoint, ClipperCreek is also a startup company that creates electric vehicle supply apparatus for the public, residential, fleet and workplace EV charging. The president is Jason France and the company is located in Auburn, California.

ClipperCreek has a projected revenue of $15.7 million. It is helping the market of electronic vehicle charging because of its HCS-40P EV Charging Station, which can work for a long period without having any problem. The charger also has a security attribute that tests the presence of the safety ground.

Schneider Electric S.E. – The France-based company has been around since 1836. Having a great leadership from its Chairman and CEO, Jean-Pascal Tricoire, Schneider Electric was able to make a difference in the electrical components industry. The company focuses on automation management, electricity distribution, and creates installation components for energy management.

Schneider Electric has an impressive estimated revenue of $27.4 billion. When it comes to EV charging, the EVlink Indoor Electric Vehicle Charging Station is deemed as a success to a lot of users. The garage tool charger is easy to maneuver because its cable holder has the capability to provide installation flexibility.

General Electric – Speaking of leadership, GE has what it takes to get that spot as it caters to different industries like power generation, renewable energy, aviation, healthcare, finance, and transportation. John Flannery is the CEO of the company located in Boston, Massachusetts.

General Electric has a projected revenue of $123.8 billion. Its GE EV Charger Indoor/Outdoor Level-2 DuraStation can transport a maximum power of 7.2 kW, giving electric vehicle a run of 10-20 miles per hour. It also has an 18-feet long cord.

Two other companies that are contributing to the market success of electronic vehicle charging are Siemens AG with its Versicharge Electric Vehicle Charger and Leviton Manufacturing with its EVB40-PST Evr-Green 400 EV Charger.

One company that has recently captured the hearts of many people is Volta Industries LLC. The San Francisco-based company is designing, installing, and maintaining a chain of free EV charging stations across the United States.

The stations can be located in Chicago, Los Angeles, Phoenix, Illinois, Hawaii, and San Diego where all are equipped with a universal connector in order to serve different types of electronic vehicles.

USA VS. UK: Battling For EV Charging Market

What is a market sale without competition from other companies or better yet, from other countries? The United Kingdom is believed to be challenging the evolving industry of EV charging around the world.

Aside from the two countries accommodating the same market, there are still differences surrounding their take on the electronic vehicle charging infrastructure. For instance, the United States has a total number of 16,541 EV charging stations, while the United Kingdom has around 130,000 EV charging points. This disparity has an effect on their overall market sales.

Another is the amount of electrical power or kilowatts that they apply to their electric vehicles. The UK can go from as low as three kilowatts to a maximum level of 22 kilowatts, and on the other hand, most EVs in the US stay only at 7.4 kilowatts or even less.

There is also a difference in the size of the electric vehicle chargers. Kristof Vereenooghe, the CEO of EVBox, said, “In Europe, people like to have smaller types of products; traditionally they prefer a smaller physical design that blends with the environment. In the US, we find that customers desire robust chargers that stand out and give visibility to the location.”

These are just some of the few differences that can be seen in the EV charging markets of the United States and the United Kingdom. Nevertheless, what matters most is their shared bold idea to make the planet healthier by not using hazardous fuels.

If the market for electric vehicle charging continues to grow, the target sale for the year 2025 might be achieved.

 

Ways Blockchain is Changing the Food Industry for the Better

Blockchain in food industry is now being utilized to keep track of food sources and transactions in the energy sector. The technology that provides transparency for all transactions has now other application on various industries.

In fact, this new technology is causing a stir in the industry especially in financial markets as it is the leading technology behind the popular bitcoin and digital-ledger systems. It has reached a point where blockchain technology is being tested in other areas and various industries.

What is BlockChain?

Blockchain is a cloud-based ledger which records the transactions done using an encryption process so as to ensure that the data is secure. However, it still allows a vast network of users to inspect and verify the data.

What makes this tech interesting is how it approaches security. There are many benefits of blockchain across industries. Its biggest strength lies in the fact that once the data is written and stored, it can’t be changed unless a majority of the participating machines agree to the change being done.

The agricultural and farming sector has already begun tapping into the possibilities. The Ukrainian government for example, is planning to use the tech to manage its crop land registry. This will help greatly as the current system is actually very susceptible to fraud which causes a lot of ownership disputes.

Possibilities of Blockchain in Food Industry

Here are some very concrete applications of blockchain in the food industry:

  • Blockchain can give consumers access to information about the meat they buy. QR codes which can be read by a smartphone can include data such as when the animal was born, whether it was injected with antibiotics, the vaccines it received, and where it grazed.
  • Blockchain will help the food supply chain become more transparent and help it become more responsive to food safety disasters. Nestlé and Unilever, two of the largest production companies in the world, are testing blockchain for this purpose. Additionally, Walmart, has completed a traceback test on mangoes to determine which farm it came from. The manual test took six days, 18 hours, and 26 minutes. Blockchain can deliver that info in 2.2 seconds. The company has already completed to blockchain projects for its chain of stores.
  • Blockchain can significantly minimize food waste. The technology can trace contaminated products quickly and identify which products belong to a certain batch of deliveries. In case of product recall, only the contaminated ones will be pulled off the shelves and destroyed. The safe ones will remain on the shelves.
  • Blockchain can help prevent fraud. This presupposes that the information in the database is accurate, but blockchain offers immense possibility in preventing food fraud. According to the Michigan State University, some $40 billion annually is lost due to fake ingredients and products. The tech facilitates the exchange of data between separate players in the food chain, and can trace individual suppliers who are knowingly or unwittingly selling fake food products.
  • Blockchain can help farmers earn more. Think of it as uber for farmers. The technology can help farmers and produce suppliers get paid more quickly and directly by people who buy from them. It can also stabilize prices during lean or peak months for a certain item. Indirectly, this can also give farmers a second income source – selling to peers or marketing their items only for direct orders. This will not only allow consumers to buy items at lower prices, it also helps them get the produce fresh from the farm.

Various industries are exploring the countless applications of blockchain technology. This bold idea can revolutionize markets and improve processes once seen as tedious and prone to human error.  In the food industry, where people’s health and lives are at stake, it pays to be 100% certain of where the food came from and how it was made. Blockchain is slowly helping achieve that.