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How a chatbot can actually change people’s habits

(EDITORIAL) So many brands are creating chatbot functions and say they’re “building” a chatbot, but think of your users as you expand into this universe – define what you’re doing first.

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It’s no secret there are a lot of chatbots these days. The latest trend: chatbots wanting to change people’s habits, and not all are created equal. As these types of bots become more prevalent, it poses the question: do they actually work? Answer: sometimes, and it depends.

Do chatbots actually affect behavior?

As a founder of an AI chatbot financial assistant, I know the opportunities and challenges that come from influencing daily behavior. When it comes to habits, you face the difficulties of say, marketing a vitamin versus a painkiller. I want to build software that will enact actual change, but let’s be real – people aren’t as motivated in the mundane, everyday decisions, because they don’t think it matters.

I’ve seen my fair share of chatbots — both impressive and crappy — come and go, and I can confidently say that chatbots/AI assistants will only work if behavioral science is implemented. This must be intentionally created throughout the software — from UX to UI to copywriting.

When there’s not an actual person on the other side of the conversation, the bot needs to use other motivating factors — otherwise, users won’t take it seriously. (Remember SmarterChild on AIM? Case in point.)

Real-life example: Open Habits

Let’s explore this further and look at new startup, Open Habits.

First off, the origin of Open Habits is pretty interesting. Twitter and Product Hunt user Aiden Buis tweeted a fun concept – a self-imposed hackathon where he would build and ship a SaaS product within 100 hours, and document every step.

It’s built as a bot within the app Telegram, so others can track your progress. But with the Open Habits bot, it isn’t geared towards a specific habit or interest. A user can track any habit they want to change. It seems like a good idea for flexibility, but in reality, this typically sets someone up for failure.

Motivations for different habits aren’t one size fits all, but specific tactics need to be used depending on the desired habit to change.

Overall, I’d give it an 7/10. For a quickly shipped software, it’s not all that bad.

But to actually create change, here are some guidelines to keep in mind:

1. Go easy on the notifications.

Let’s look at a software that fails at this, MyFitnessPal. I kind of shiver just thinking about the notifications I used to receive. An everyday notification typically means someone will turn off your notifications or flat-out ignore them. Make the notifications actually helpful, not constant or annoying, and for the love of God, please space out the timing.

2. Show the long-term picture for daily habits.

Show your users what they’re doing does matter and does lead to big change.

For example: If you’re talking about weight loss, show how swapping one dessert for fruit once a week can equate to X or Y calories or pounds lost a year. If it’s financial habits, show how saving even $1 a day can grow your financial future into $X. (Acorns does an excellent job of this.)

3. Do your research on favorable or unfavorable language.

If you’re trying to change someone’s habits, prepare to get to know as many experts as possible in your field. Read all the books, meet all the professors, and get to know all the researchers that study far beyond what you’re doing. Prime example: financial app users hate the term “budgeting” because it’s associated with negative feelings, and we only knew this because of This is why it’s crucial to become best friends with the leaders in your industry.

As always, this is simply a starting point for guidelines to keep in mind whether you’re building or just using a chatbot. Look at the competitors, see what works best for you and what motivates you, then go from there.

Elise Graham Kennedy is a staff writer at The American Genius and Austin-based digital strategist. She's a seasoned entrepreneur, started and sold two companies, and was on a TV show for her app. You can usually find her watching The Office on her couch with her dog and husband.

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4 ways startups prove their investment in upcoming technology trends

(TECH NEWS) Want to see into the future? Just take a look at what technology the tech field is exploring and investing in today — that’s the stuff that will make up the world of tomorrow.

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Woman testing VR technology

Big companies scout like for small ones that have proven ideas and prototypes, rather than take the initial risk on themselves. So startups have to stay ahead of technology by their very nature, in order to be stand-out candidates when selling their ideas to investors.

Innovation Leader, in partnership with KPMG LLP, recently conducted a study that sheds light onto the bleeding edge of tech: The technologies that the biggest companies are most interested in building right now.

The study asked its respondents to group 16 technologies into four categorical buckets, which Innovation Leader CEO Scott Kirsner refers to as “commitment level.”

The highest commitment level, “in-market or accelerating investment,” basically means that technology is already mainstream. For optimum tech-clairvoyance, keep your eyes on the technologies which land in the middle of the ranking.

“Investing or piloting” represents the second-highest commitment level – that means they have offerings that are approaching market-readiness.

The standout in this category is Advanced Analytics. That’s a pretty vague title, but it generally refers to the automated interpretation and prediction on data sets, and has overlap with Machine learning.

Wearables, on the other hand, are self explanatory. From smart watches to location trackers for children, these devices often pick up on input from the body, such heart rate.

The “Internet of Things” is finding new and improved ways to embed sensor and network capabilities into objects within the home, the workplace, and the world at large. (Hopefully that doesn’t mean anyone’s out there trying to reinvent Juicero, though.)

Collaboration tools and cloud computing also land on this list. That’s no shock, given the continuous pandemic.

The next tier is “learning and exploring”— that represents lower commitment, but a high level of curiosity. These technologies will take a longer time to become common, but only because they have an abundance of unexplored potential.

Blockchain was the highest ranked under this category. Not surprising, considering it’s the OG of making people go “wait, what?”

Augmented & virtual reality has been hyped up particularly hard recently and is in high demand (again, due to the pandemic forcing us to seek new ways to interact without human contact.)

And notably, AI & machine learning appears on rankings for both second and third commitment levels, indicating it’s possibly in transition between these categories.

The lowest level is “not exploring or investing,” which represents little to no interest.

Quantum computing is the standout selection for this category of technology. But there’s reason to believe that it, too, is just waiting for the right breakthroughs to happen.

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Tech News

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.

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Tech News

Google is giving back some privacy control? (You read that right)

(TECH NEWS) In a bizarre twist, Google is giving you the option to opt out of data collection – for real this time.

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Open laptop on desk, open to map privacy options

It’s strange to hear “Google” and “privacy” in the same sentence without “concerns” following along, yet here we are. In a twist that’s definitely not related to various controversies involving the tech company, Google is giving back some control over data sharing—even if it isn’t much.

Starting soon, you will be able to opt out of Google’s data-reliant “smart” features (Smart Compose and Smart Reply) across the G-Suite of pertinent products: Gmail, Chat, and Meet. Opting out would, in this case, prevent Google from using your data to formulate responses based on your previous activity; it would also turn off the “smart” features.

One might observe that users have had the option to turn off “smart” features before, but doing so didn’t disable Google’s data collection—just the features themselves. For Google to include the option to opt out of data collection completely is relatively unprecedented—and perhaps exactly what people have been clamoring for on the heels of recent lawsuits against the tech giant.

In addition to being able to close off “smart” features, Google will also allow you to opt out of data collection for things like the Google Assistant, Google Maps, and other Google-related services that lean into your Gmail Inbox, Meet, and Chat activity. Since Google knowing what your favorite restaurant is or when to recommend tickets to you can be unnerving, this is a welcome change of pace.

Keep in mind that opting out of data collection for “smart” features will automatically disable other “smart” options from Google, including those Assistant reminders and customized Maps. At the time of this writing, Google has made it clear that you can’t opt out of one and keep the other—while you can go back and toggle on data collection again, you won’t be able to use these features without Google analyzing your Meet, Chat, and Gmail contents and behavior.

It will be interesting to see what the short-term ramifications of this decision are. If Google stops collecting data for a small period of time at your request and then you turn back on the “smart” features that use said data, will the predictive text and suggestions suffer? Only time will tell. For now, keep an eye out for this updated privacy option—it should be rolling out in the next few weeks.

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