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Artificial Intelligence in Home Robots – Current and Future Use-Cases

The International Federation of Robotics reports that the U.S. service robot industry, which includes both industrial and domestic sectors, is a $5.2 billion market.

The video below briefly explains the product and its features: Sharp Corporation’s RX-V100 RX-V100 is a cleaning robot embedded with a speech recognition AI engine to enable tasks such as reporting the current status of the robot through a combination of blinking lights and spoken messages.

In addition to this, the product comes with a set of predefined messages and responses to simulate simple conversations, such as “Good Morning” or “How’s it going?” Here’s a 3-minute marketing video from Sharp, featuring some of the basic use cases and capabilities of the RX-V100 model: Bosch Roxxter Bosch launched its Roxxter range of robotic vacuum cleaners that leverage AI to draw interactive maps of their environment.

For example, a user can activate the Roxxter robot by commanding Alexa: “Alexa, move the Home Connect robot vacuum to the living room.” This is a 2-minute video from Bosch showing the product’s features: Other key players in this home cleaning robot segment include Dyson, Samsung and Neato.

2017 World Robotics report estimates that entertainment robots (toys or hobby robots) sold 2.5 million units in 2017, bringing in a revenue of $1.1 billion.

It can “understand” the users’ facial expressions, vocal intonations and verbal patterns via computer vision, microphone array technology and an “emotion character engine,” and proactively start conversations rather than reacting to users’ commands.

The video below suggests that Olly can simulate human emotions like empathy (it appears to “see” a user resting his head on the couch and asks him, “Long day?”) and predict the music the user might want to listen to according to their mood.

short video of the product is presented below: Deep Sentinel Deep Sentinel is an American company that aims to sell AI-powered home security and surveillance solutions, which can “predict and disrupt crimes before they occur.” The company claims that it has “optimized AI technology” to trigger alert systems even before a potential crime occurs.

Paraphrasing a 2017 TechCrunch article about the firm: Deep sentinel aims to expand the home security and surveillance market by moving the security line to the perimeter of a property rather than the perimeter of a home.

(We’ve covered AI security applications in much greater depth in our complete article on AI in security.) Minnesota-based VC firm Loup Ventures estimates (with the IFR) that by 2025 the robotic vacuum and lawn mower hardware markets will grow to $2.6 billion and $1 billion, respectively.

Stanford report titled “Artificial Intelligence and Life in 2030” predicts that integration of emerging AI technologies in robotics—such as speech recognition, natural language understanding, and image labeling—would enable faster adoption of domestic robots by 2030.

One could imagine many such personal data issues: As home robots become ubiquitous, many of these hypothetical risks are likely to come to public attention, and it seems likely that regulations and rules will be establish to protect users (though it’s likely that companies will be incentivized to collect and use the data anyway, if it behooves them to do so).

Everyday Examples of Artificial Intelligence and Machine Learning

With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you’re already using—right now?

You’ve also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases.  We distinguish between AI and machine learning (ML) throughout this article when appropriate.

According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014—more than a full work week per year, with an estimated $160 billion in lost productivity.

driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction;

Engineering Lead for Uber ATC  Jeff Schneider discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary.

Glimpse into the future In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents, more efficient ride sharing to  reduce the number of cars on the road by up to 75%, and smart traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.

“filter out messages with the words ‘online pharmacy’ and ‘Nigerian prince’ that come from unknown addresses”) aren’t effective against spam, because spammers can quickly update their messages to work around them.

In a research paper titled, “The Learning Behind Gmail Priority Inbox”, Google outlines its machine learning approach and notes “a huge variation between user preferences for volume of important mail…Thus, we need some manual intervention from users to tune their threshold.

The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.” Glimpse into the future Can your inbox reply to emails for you?

Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016, 10% of mobile Inbox users’ emails were sent via smart reply.

A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm.

– Credit Decisions Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers.

In early 2016, Wealthfront announced it was taking an AI-first approach, promising “an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before.” While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings.

In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks—ML algorithms that mimic the structure of the human brain—to power facial recognition software.

In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages.” DeepText is used in Facebook Messenger to detect intent—for instance, by allowing you to hail an Uber from within the app when you message “I need a ride” but not when you say, “I like to ride donkeys.” DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.

– Pinterest Pinterest uses computer vision, an application of AI where computers are taught to “see,” in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.

– Instagram Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”).

This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.

A few months later, it opened its messenger platform to developers, allowing anyone to build a chatbot and integrate’s bot training capability to more easily create conversational bots.

–Recommendations You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and  “customers who bought this item also bought”, as well as via personalized recommendations on the home page,  bottom of item pages, and through email.

While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).

Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions.

By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.

We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search, even with users who are logged out, so this is well within the realm of possibility for retailers.

however, a month later Amazon’s press release boasted a 9x increase in Echo family sales over the previous year’s holiday sales, suggesting that 5 million sold is a significant underestimate.

For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and  bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.

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