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Internet of Things and deep learning: How your devices are getting smarter

(TECH NEWS) The latest neural network from Massachusetts Institute of Technology shows a great bound forward for deep learning and the “Internet of Things.”

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Woman using smart phone to control other devices in home, connected to deep learning networks

The deep learning that modifies your social media and gives you Google search results is coming to your thermostat.

Researchers at the Massachusetts Institute of Technology (MIT) have developed a deep learning system of neural networks that can be used in the “Internet of Things” (IoT). Named MCUNet, the system designs small neural networks that allow for previously unseen speed and accuracy for deep learning on IoT devices. Benefits of the system include energy savings and improved data security for devices.

Created in the early 1980s, the IoT is essentially a large group of everyday household objects that have become increasingly connected through the internet. They include smart fridges, wearable heart monitors, thermostats, and other “smart” devices. These gadgets run on microcontrollers, or computer chips with no processing system, that have very little processing power and memory. This has traditionally made it hard for deep learning to occur on IoT devices.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” said Song Han, Assistant Professor of Computer Science at MIT who is a part of the project, “Companies like Google and ARM are all working in this direction.”

In order to achieve deep learning for IoT connected machines, Han’s group designed two specific components. The first is TinyEngine, an inference engine that directs resource management similar to an operating system would. The other is Tiny NAS, a neural architecture search algorithm. For those not well-versed in such technical terms, think of these things like a mini Windows 10 and machine learning for that smart fridge you own.

The results of these new components are promising. According to Han, MCUNet could become the new industry standard, stating that “It has huge potential.” He envisions the system has one that could help smartwatches not just monitor heartbeat and blood pressure but help analyze and explain to users what that means. It could also lead to making IoT devices far more secure than they are currently.

“A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

It will still be a while until we see smart devices with deep learning capabilities, but it is all but inevitable at this point—the future we’ve all heard about is definitely on the horizon.

Patrick Auger is a management consultant and entrepreneur who resides in Austin, Texas. He has a Bachelor of Arts in Business Management from Western Illinois University, and is the Founder and Principal Consultant at Auger Consulting Group, LLC. When he's not writing for The American Genius, he's writing about the business of Mixed Martial Arts for The Body Lock or learning how to cook, one burnt recipe at a time.

Tech News

Chatbots: Are they still useful, or ready to be retired?

(TECH NEWS) Chatbots have proven themselves to be equally problematic as they are helpful – is it time to let them go the way of the floppy disk?

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Man texting chatbots leaning against a brick wall.

All chatbots must die. I’d like to say it was fun while it lasted, but was it really?

I understand the appeal, truly. It’s a well established 21st century business mantra for all the side hustlers and serial entrepreneurs out there: “Automation is the key to scaling.” If we can save time, labor, and therefore money by automating systems, that means we have more time to build our brands and sell our goods and services.

Automation makes sense in many ways, but not all automation tools were created equal. While many tools for automation are extremely effective and useful, chatbots have been problematic from the start. Tools for email marketing, social media, internal team communication, and project management are a few examples of automation that have helped many a startup or other small business kick things into high gear quickly, so that they can spend time wooing clients and raising capital. They definitely have their place in the world of business.

However promising or intriguing chatbots seemed when they were shiny and new, they have lost their luster. If we have seen any life lesson in 2020, it is that humans are uniquely adept at finding ways to make a mess of things.

The artificial intelligence of most chatbots has to be loaded, over time, into the system, by humans. We try to come up with every possible customer-business interaction to respond to with the aim of being helpful. However, language is dynamic, interactive, with near infinite combinations, not to mention dialects, misspellings, and slang.

It would take an unrealistic amount of time to be able to program a chatbot to compute, much less reply to, all possible interactions. If you don’t believe me, consider your voice-activated phone bot or autocorrect spelling. It doesn’t take a whole lot to run those trains off the rails, at least temporarily. There will always be someone trying to confuse the bots, to get a terse, funny, or nonsensical answer, too.

Chatbots can work well when you are asking straightforward questions about a single topic. Even then, they can fall short. A report by AI Multiple showed that some chatbots were manipulated into expressing agreement with racist, violent, or unpatriotic (to China, where they were created) ideas. Others, like CNN and WSJ, had problems helping people unsubscribe from their messages.

Funny, shocking, or simply unhelpful answers abound in the world of chatbot fails. People are bound to make it messy, either accidentally or on purpose.

In general, it feels like the time has come to put chatbots out to pasture. Here are some helpful questions from azumbrunnen.me to help you decide when it’s worth keeping yours.

  1. Is the case simple enough to work on chatbot? Chatbots are good with direct and short statements and requests, generally. However, considering that Comcast’s research shows at least 1,700 ways to say “I want to pay my bill,” according to Netomi, the definition of “simple enough” is not so simple.
  2. Is your Natural Language Processor capable and sophisticated enough? Pre-scripted chatbots are often the ones to fail more quickly than chatbots built with an NLP. It will take a solid NLP to deal with the intricacies of conversational human language.
  3. Are your users in chat based environments? If so, then it could be useful, as you are meeting your customers where they are. Otherwise, if chatbots pop up whenever someone visits your website or Facebook page, it can really stress them out or turn them off.

I personally treat most chatbots like moles in a digital whack-a-mole game. The race is on to close every popup as quickly as possible, including chatbots. I understand that from time to time, in certain, clearly defined and specific scenarios, having a chatbot field the first few questions can help direct the customer to the correct person to resolve their problems or direct them to FAQs.

They are difficult to program within the expansiveness of the human mind and human language, though, and a lot of people find them terribly annoying. It’s time to move on.

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Tech News

Get all your digital organization in one place with Routine

(TECH NEWS) Routine makes note-taking and task-creating a lot easier by merging all your common processes into one productivity tool.

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A desk with a laptop, notepad, smartphone, and cup of coffee settled into an organized routine.

Your inbox can either be your best friend or your worst enemy. Without organization, important emails with tasks, notes, and meetings can become a trash pile pretty quickly. Luckily, there are a lot of tools that aim to help you improve your efficiency, and the latest to add to that list is Routine.

Routine is a productivity app that combines your tasks, notes, and calendar into one easy-to-use app so you can increase your performance. Instead of having to switch between different apps to jot down important information, create to-do lists, and glance at your calendar, Routine marries them all into one cool productivity tool. By simply using a keyboard shortcut, you can do all these things.

If you receive an email that contains an actionable item, you can convert that email into a task you can view later. Tasks are all saved in your inbox, and you can even schedule a task for a specific day. So, if Obi-Wan wants to have Jedi lessons on Thursday, you can schedule your Force task for that day. Likewise, chat messages that need follow-up can also be converted into tasks and be scheduled.

To enrich your tasks, notes can be attached to them. In your notes, you can also embed checkboxes, which are tasks of their own. And if you have tasks that aren’t coming from your inbox, you can import them from other services, such as Gmail, Notion, and Trello.

To make sure you can stay focused on the events and tasks at hand, Routine makes it easy to take everything in. By using the tool’s keyboard-controlled console, you can access your dashboard to quickly see what tasks need to be addressed, what’s on your calendar, and even join an upcoming Zoom session and take notes about the meeting.

Routine is available for macOS, iOS, web, and Google accounts only. Overall, the app centralizes notes and tasks by letting you create and view everything in one place, which helps make sure you stay on top of things. Currently, Routine is still in beta, but you can get on a waitlist to test the product out for yourself.

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Tech News

The paradox of CAPTCHAs: Too smart for humans vs AI?

(TECH NEWS) AI is catching up to our cybersecurity technology and often tricking humans too — so what’s next for CAPTCHAs and the internet?

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Person using phone with laptop to verify CAPTCHAs and code.

We’ve all encountered it before: The occasional robot test that feels impossible to beat. If you’ve felt like these tests, also known as CAPTCHAs, have gotten harder in the last couple of years, you aren’t wrong—and the reason is as ironic as it is baffling.

Simply put, AI are just as good as—and often better than—humans at completing CAPTCHAs in their classic format. As machine learning and AI become more advanced, the fundamental human attributes that make consistent CAPTCHA formats possible become less impactful, raising the question of how to determine the difference between AI and humans in the future.

The biggest barrier to universal CAPTCHA doctrine is purely cultural. Humans may share experiences across the board, but such experiences are typically basic enough to fall victim to the same machine learning which has rendered lower-level CAPTCHAs moot. Adding a cultural component to CAPTCHAs could prevent AI from bypassing them, but it also might prevent some humans from understanding the objective.

Therein lies the root of the CAPTCHA paradox. Humans are far more diverse than any one test can possibly account for, and what they do have in common is also shared by—you guessed it—AI. To create a truly AI-proof test would be to alienate a notable portion of human users by virtue of lived experience. The irony is palpable, but one can only imagine the sheer frustration developers are going through in attempting to address this problem.

But all isn’t lost. While litmus tests such as determining the number of traffic cones in a plaza or checking off squares with bicycles (but not unicycles, you fool) may be beatable by machines, some experts posit that “human entropy” is almost impossible to mimic—and, thus, a viable solution to the CAPTCHA paradox.

“A real human being doesn’t have very good control over their own motor functions, and so they can’t move the mouse the same way more than once over multiple interactions,” says Shuman Ghosemajumder, a former click fraud expert from Google. While AI could attempt to feign this same level of “entropy”, the odds of a successful attempt appear low.

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