As a business owner you may have heard quite a bit about Artificial Intelligence and its benefits for business. However, you may not be aware that adding tools based on integrated machine learning, deep learning algorithms and other products is not as difficult as it sounds. Indeed, you may not even be aware that some of these are examples of AI.
Chatbots and virtual assistants
Have you visited a services website recently and had a box pop up offering to have a chat with you? Chatbots and virtual assistants are appearing on more and more websites. It isn’t difficult to find a chatbot service for you business and you can get a writer to provide you with a bespoke script that suits the tone and style of the rest of your website.
Online courses
It doesn’t have to cost you anything to get taught by the best. For example, Udacity offers a free Intro to AI course. Stanford University has a AI: Principles and Techniques course, and there are many others, including a Microsoft’s Cognitive Toolkit and MonkeyLearn’s ‘Gentle Guide to Machine Learning’.
Know what you want to do with AI
Once you’ve learnt the AI basics, it’s time to establish how AI can help your business. Think about how you can add AI capabilities to your existing products and services and look for ways n which AI can solve problems and add value.
Bring in the experts
Once you’ve identified some goals in your business where AI provides a valuable solution, it is probably time to organise consultations with AI experts. Setting up a pilot project that can be evaluated over a two to three months period, is one way to do it and bring in consultants to work with a small internal team. Once the pilot has been completed, you’ll be able to decide whether or not to take it forward for the long term.
Integrate AI in daily work routines
Once you have AI on board, make sure all workers have a tool to make AI part of their daily routine, rather than something that replaces it. AI scares some employees who feel threatened by it in the sense it might replace them, so it’s important to demonstrate that it is a help to them instead.
AI can really improve your chances of success and help teams to work more efficiently — so it’s time to get on board with the bots in business.
You, and probably a large percentage of your friends, are likely to have received an email from someone in Africa who needs you to help them get millions out of the country and if you help them you will be receive payment for your services. This scam is old, but it is persistent; you have to give the scammers that. There are plenty more of these types of emails, some of them more subtle than others, such as the ones from Paypal or Amazon that look like the real thing. You have to look closely to realise they aren’t from those companies at all, but from impostors.
$670 million lost in crypto fraud
There is also a new breed of fraud perpetrated by crypto scammers who have so far relied on the fact that “short cons carried out using crypto are hard to detect and almost impossible to trace,” as Jonas Karlberg writes in Medium. He also reveals that an estimated $670 million has been lost through crypto fraud in the first quarter of 2018 alone, which shows the extent of the problem.
The most common way that crypto cons work is through phishing emails. An old tool for a new game, you might say. One example is where a ‘victim’ is sent to a cloned version of a crypto project’s social media account, where they are likely to be enticed to open their wallet address in return for an incentive, such as free tokens. The person then eventually realises they haven’t received a receipt for their payment, but by then the funds have gone and so have the scammers.
AI provides an army of protector bots
However, Artificial Intelligence (AI) is offering ways to fight this online fraud. One company, AmaZix is using bots to fight the ‘con’ bots. These bots can delete content and ban users before the public have even spotted them. Karlberg describes the management by moderators of the ongoing battle in online crypto communities as “generals presiding over enormous AI battles,” with the ‘good’ bots defending users against the scambots.
AI is developing in power and complexity and it is enabling cyber security firms to trawl even larger areas of digital space. The people operating the scambots don’t have the resources to match the funds put into developing protector bots by security firms, which does give the good guys an advantage. Of course, nobody in this sector can ever rest, because the con men will always be looking for a new way to break through the battlements, but as blockchain technology gains in mass adoption, the AI will become more sophisticated and powerful, which is good news for the public and bad news for fraudsters.
As someone who grew up and was educated in Canada, even though I no longer live there, I’m always keen to keep up with what’s happening. So, I was fascinated to read this article by Trenton Paul, a tech enthusiast, on the gap between Toronto and Quebec. He describes Toronto as being all set to become the country’s first smart city, while Quebec has been overrun by bitcoin miners, which appears to be to the detriment of the city. He puts forward an interesting theory — that Canada can’t find a balance when it comes to implementing new technology.
In Quebec, he takes us on a brief tour of an old factory that is now owned by Bitfarms, one of North America’s largest cryptocurrency mining operations. Here there are 7,000 machines doing the same repetitious task, and their number is expected to double this summer. Fans whirr everywhere trying to keep the machines cool and he describes the working conditions as akin to “working in an IT sauna.” He points out that maintaining this process uses up more energy than the nearby Montreal Canadiens’ hockey arena. And, the local energy company Hydro-Quebec has been trying to attract more mining operations to the city, and the mining operations have been flocking there.
What’s the problem in Quebec?
Quite simply this: the number of applications from mining firms wanting to set up in Quebec could potentially make the city a global hub for crypto mining, which sounds great, until you realise that if all of them were in full operation they could cause the collapse of the electricity supply to the Quebec region.
Bitcoin miners also prefer to use clean energy, which means they avoid countries like the U.S. and China where fossil fuels are in wider use. Hydro-Quebec promises that mining operations there are fuelled by hydroelectric power and that the power used for the mining companies is “surplus” — an extra 100 terawatts of low-impact energy. The problem is this, as Trenton Paul says: “as demand grows and more energy is needed to power these machines, the power supply available will not be able to sustain much longer.”
Some joined up thinking is needed
Meanwhile in Toronto, city officials are getting ready to promote it as the country’s first smart city, with Alphabet’s subsidiary Sidewalk Labs planning a timeline for building a smart complex.
As we are still in the early days of this technological ‘gold rush’, it is impossible to say how this will all pan out to Canada’s benefit, but what is clearly required is a ‘One Canada’ policy that brings balance to the implementation of new technology and offers some sound, joined up thinking.
The 3D-printed artificial neural network can be used in medicine, robotics and security
The network, composed of a series of polymer layers, works using light that travels through it. Each layer is 8 centimeters square.
A team of UCLA electrical and computer engineers has created a physical artificial neural network — a device modeled on how the human brain works — that can analyze large volumes of data and identify objects at the actual speed of light. The device was created using a 3D printer at the UCLA Samueli School of Engineering.
Numerous devices in everyday life today use computerized cameras to identify objects — think of automated teller machines that can “read” handwritten dollar amounts when you deposit a check, or internet search engines that can quickly match photos to other similar images in their databases. But those systems rely on a piece of equipment to image the object, first by “seeing” it with a camera or optical sensor, then processing what it sees into data, and finally using computing programs to figure out what it is.
The UCLA-developed device gets a head start. Called a “diffractive deep neural network,” it uses the light bouncing from the object itself to identify that object in as little time as it would take for a computer to simply “see” the object. The UCLA device does not need advanced computing programs to process an image of the object and decide what the object is after its optical sensors pick it up. And no energy is consumed to run the device because it only uses diffraction of light.
New technologies based on the device could be used to speed up data-intensive tasks that involve sorting and identifying objects. For example, a driverless car using the technology could react instantaneously — even faster than it does using current technology — to a stop sign. With a device based on the UCLA system, the car would “read” the sign as soon as the light from the sign hits it, as opposed to having to “wait” for the car’s camera to image the object and then use its computers to figure out what the object is.
Technology based on the invention could also be used in microscopic imaging and medicine, for example, to sort through millions of cells for signs of disease.
“This work opens up fundamentally new opportunities to use an artificial intelligence-based passive device to instantaneously analyze data, images and classify objects,” said Aydogan Ozcan, the study’s principal investigator and the UCLA Chancellor’s Professor of Electrical and Computer Engineering. “This optical artificial neural network device is intuitively modeled on how the brain processes information. It could be scaled up to enable new camera designs and unique optical components that work passively in medical technologies, robotics, security or any application where image and video data are essential.”
The process of creating the artificial neural network began with a computer-simulated design. Then, the researchers used a 3D printer to create very thin, 8 centimeter-square polymer wafers. Each wafer has uneven surfaces, which help diffract light coming from the object in different directions. The layers look opaque to the eye but submillimeter-wavelength terahertz frequencies of light used in the experiments can travel through them. And each layer is composed of tens of thousands of artificial neurons — in this case, tiny pixels that the light travels through.
Together, a series of pixelated layers functions as an “optical network” that shapes how incoming light from the object travels through them. The network identifies an object because the light coming from the object is mostly diffracted toward a single pixel that is assigned to that type of object.
The researchers then trained the network using a computer to identify the objects in front of it by learning the pattern of diffracted light each object produces as the light from that object passes through the device. The “training” used a branch of artificial intelligence called deep learning, in which machines “learn” through repetition and over time as patterns emerge.
“This is intuitively like a very complex maze of glass and mirrors,” Ozcan said. “The light enters a diffractive network and bounces around the maze until it exits. The system determines what the object is by where most of the light ends up exiting.”
In their experiments, the researchers demonstrated that the device could accurately identify handwritten numbers and items of clothing — both of which are commonly used tests in artificial intelligence studies. To do that, they placed images in front of a terahertz light source and let the device “see” those images through optical diffraction.
They also trained the device to act as a lens that projects the image of an object placed in front of the optical network to the other side of it — much like how a typical camera lens works, but using artificial intelligence instead of physics.
Because its components can be created by a 3D printer, the artificial neural network can be made with larger and additional layers, resulting in a device with hundreds of millions of artificial neurons. Those bigger devices could identify many more objects at the same time or perform more complex data analysis. And the components can be made inexpensively — the device created by the UCLA team could be reproduced for less than $50.
While the study used light in the terahertz frequencies, Ozcan said it would also be possible to create neural networks that use visible, infrared or other frequencies of light. A network could also be made using lithography or other printing techniques, he said.
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The study’s others authors, all from UCLA Samueli, are postdoctoral scholars Xing Lin, Yair Rivenson, and Nezih Yardimci; graduate students Muhammed Veli and Yi Luo; and Mona Jarrahi, UCLA professor of electrical and computer engineering.
The research was supported by the National Science Foundation and the Howard Hughes Medical Institute. Ozcan also has UCLA faculty appointments in bioengineering and in surgery at the David Geffen School of Medicine at UCLA. He is the associate director of the UCLA California NanoSystems Institute and an HHMI professor.
Mehran Muslimi 