Chatbots are a natural fit for enhancing customer service, especially in an online or mobile setting, and we run into them on websites, in hospitals, on social media, and in mobile apps. There is room for this AI-enabled application in almost every business vertical, but there are pros and cons to be aware of when developing your first chatbot.

Chatbots in Customer Support

Customer support can be grueling. It requires businesses to provide friendly, responsive, and quick help at almost any time of the day. Because of the large investment involved in hiring people and building resources, it is difficult to build an excellent customer support team overnight.

It takes a lot of constant training and ongoing optimization to finetune your support representatives to meet all key performance indicators, like time-to-first-response, time-to-close-ticket, and customer satisfaction or feedback. Instead of skimping on one of your most important customer-facing departments, consider what a chatbot can unlock for you and your customers. Whether your company is a Fortune 500 company, doctor’s office, or a corner grocery store, your business can create and deploy a chatbot quickly and easily without a massive investment.

Benefits of Chatbots in Business

Increase Customer Happiness

Every company is in the business of making their customers happy. It’s what keeps profits steady and brand recognition strong. Thankfully, chatbots fit right into that notion as they are 100% customer-centric.

Chatbots work to answer user questions as fast as possible to reach a resolution on the question at hand. They help customers with the same efficiency and response rate, unlike humans which respond to various queries with different response times. This reliability can give customers more consistent expectations of interacting with your company.

If done right, chatbots can improve customer satisfaction. For example, an customer service representative based in Los Angeles may not be able to speak Japanese to help a customer, but a chatbot can easily switch to a different language on the fly. This unlocks the added benefit of reaching many more customers without having to hire interpreters or native speakers.

Leave an Impression on Customers

Besides serving up a consistent experience that customers can count on, chatbots offer customers the opportunity to “ooh” and “aah” over your company’s use of the most cutting-edge AI and machine learning technology. Many customers enjoy interacting with a chatbot, and engagement rates have been increasing steadily.

In 2019, nearly 40% of retail customers engaged with a chatbot in their search for an answer. When chatbots work well, they can bolster your brand image. They can also help customers see your company as an industry leader.

Available Around-the-Clock

Unlike people, chatbots can be “on” 24/7 without feeling exhausted or needing a break. They can help a customer whenever they need it and from wherever they’re located. Chatbots can stay on indefinitely and use customer feedback to continuously improve their success rates.

Generate Better Leads

Besides helping people with their questions and issues, chatbots can also generate leads. Add in a spam filter, and you can even generate better leads than your contact forms. In on-demand delivery apps, chatbots are often used to take orders and process payments from new customers. This can reduce the amount of time it takes to get a new lead to purchase a product from your company.

More Cost-Effective

Because chatbots don’t require healthcare or sick days, chatbots are much cheaper in the long run compared to a traditional customer support team. They use fewer resources like office space, money, and equipment. Chatbots cost a one-time investment fee, and they need minimal maintenance to continue performing.

Downsides of Chatbots

Talking to a Computer

At the end of the day, most chatbots can’t help customers shake the feeling that they’re talking to an automated AI application. For many customers, their problems can only be solved by a human, and this often includes problems with a purchase, shipping, or credit card charge. For these customers, interacting with a chatbot can feel futile and frustrating.

Although natural language processing technology has come a long way in the past few years, we still haven’t reached a point where chatbots feel human. Fluid language isn’t possible yet. Even the best chatbots can’t make customers feel like they’re talking to a human.

Difficult to Develop

Chatbots can be difficult to design and develop, especially if your company’s use case is complex. When adding machine learning technology into the mix, chatbot development can get downright arduous. As it stands, companies need to spend a lot of time, effort, and server space in creating their chatbot.

Most businesses need chatbots for simpler needs, however, like customer support which can run on pattern-based algorithms. There are already no-code or low-code platforms that offer brands the ability to create a custom chatbot. As technology keeps advancing, it’ll get easier and easier to create and deploy chatbots.

Ongoing Maintenance Schedules

Chatbots require updating and maintenance. Someone should look into the chatbot’s failed customer support tickets to make adjustments where necessary to avoid failing to resolve a ticket about the same topic again. As user questions and issues grow, take some time to add these new topics and their associated solutions into the chatbot’s knowledge bank. The time and effort will be worth it: this work can keep your brand image strong.

Developing Your Chatbot

Creating and deploying a chatbot is getting easier, and more companies are taking advantage of this technology by combining it with AI and machine learning. Don’t get left behind — a well-maintained chatbot with a high success rate in answering questions can strengthen your brand image and leave a great impression on your customers.

Have you interacted with an excellent chatbot? What company’s chatbot was it, and what did you take away from the experience? Let us know in the comments below!

We’ve barely just begun 2021, but it’s already shaping up to be an amazing year for Dogtown Media. We were recently featured as a top machine learning developer by Techreviewer.co! Thanks so much to our clients, team, and community for your continued support — you’re the ones who make this possible.

Techreviewer is a trusted B2B information platform that regularly conducts market research across numerous sectors, such as development, design, and marketing. Its meticulous methodology quickly helps companies identify high-quality services for a variety of complex technical tasks. Whether you’re searching for experts in AI app development, the Internet of Things, or business analysis, Techreviewer’s analysts can expedite the process of finding the best technology partner for your business needs.

For Techreviewer’s list of 2021’s top machine learning companies, the review hub evaluated each contender by examining its demonstrated expertise, experience, quality of services, and reliability to deliver products that went above and beyond clients’ requirements. That last component was especially crucial in elucidating the top players in this sector; the product must leverage key aspects of machine learning to help transform a client’s digital presence.

Over 500 companies were considered for this prestigious award. We’re extremely proud to be able to say that we made the cut after quite a few time-intensive assessments!

Besides being recognized as one of the best machine learning app developers of 2021, we’re also grateful to have been featured as one of 2021’s best iPhone app developers as well as a top mobile app developer in Los Angeles by Digital.com!

With this award, we couldn’t be more excited to see what else is in store for our company in the new year! Thanks to Techreviewer for recognizing our hard work. And thanks again to our clients, team, and community — we couldn’t have done this without you!

About TechReviewer.co

Techreviewer a trusted analytical hub that carries out studies and compiles the lists of top development, design and marketing companies. The platform helps to find the best companies that provide high-quality IT services for technical support, development, system integration, AI, Big Data, and business analysis. As a result of objective market analysis, the Techreviewer platform determines the most successful and reliable IT companies and makes top ratings for each of the service categories. Techreviewer’s ranking lists help organizations select the right technology partner for their business needs.

You might be wondering how a technology that’s set up to understand linguistics is able to understand genetics. It turns out that many characteristics of biology can be interpreted similarly to words and sentences. According to Bonnie Berger, a computational biologist at MIT and an author of the research, “We’re learning the language of evolution.”

Finding Hidden Viruses

Berger and her research team aren’t the first to train their NLP algorithms on protein sequences and genetic codes. Researchers from Salesforce and teams led by geneticist George Church are also chipping away at this interesting idea. Berger’s team takes it a bit further by using NLP to predict virus mutations that antibodies would fail to detect, known as viral immune escape.

At the foundation of Berger’s research is an analogy: the immune system’s interpretation of a virus is similar to how humans interpret a sentence. The team uses grammar and semantics (or the meaning of the speech) to interpret the genetic and evolutionary fitness of a virus. An infectious and successful virus is grammatically correct, whereas an unsuccessful virus is grammatically incorrect. On the other hand, viruses with different mutations have different semantics, so a virus with a divergent meaning might need different antibodies to find it.

The research team used an LSTM, an AI application that utilizes neural networks, which can be trained on less data than traditional NLP tools while maintaining strong performance. Generally, NLP works by transforming words into mathematical entities, so words with similar meanings naturally translate into similar entities. This is called an embedding, and, for viruses, in particular, the NLP algorithms grouped them based on the similarities of their embedding which simplifies the similarities between mutations for researchers.

The Fine Print

Using the LTSM software, the NLP model was trained on thousands of genetic sequences from three different viruses. There were 60,000 unique sequences for a strain of HIV, 45,000 for a strain of influenza, and between 3,000 and 4,000 sequences for a strain of COVID-19. Brian Hie, a graduate student at MIT who worked on building the models, explains, “There’s less data for the coronavirus because there’s been less surveillance.”

The main goal for the research is to find mutations that an immune system might overlook which entails looking for a changed meaning that’s not grammatically incorrect. The system looks for similar grammatical structures but very different meanings, and it flags mutations for review if their meanings have changed the most. To test this approach, the team used a machine learning application that scores accuracy for predictions between 0.5 (score basically due to chance) and 1 (score is perfect).

The team took the top mutations detected by the NLP algorithm and checked them against real viruses in a lab to see how many were escape mutations. The accuracy scores ranged from 0.69 for HIV to 0.85 for coronavirus. According to the team, these results are better than results achieved by state-of-the-art algorithms.

Thinking Ahead

For now, the research is more about pushing the limits of technology and applying it in a new way to biology. But in the future, the research has the potential to make an impact in public health. Knowing the severity of mutations and the newest mutations can make it easier for public health officials and hospitals to plan ahead for the upcoming flu season, giving them a sense of how well the public’s immune system is going to work this year.

The team ran their models on the new COVID-19 variants out of the UK, Denmark, South Africa, Singapore, and Malaysia. They found that there is a high potential for immune escape in all of the variants. However, the model missed a change in the South African variant that may have helped the team catch a loophole in their algorithm. Berger says she thinks the problem is that “it consists of multiple mutations, and we believe a combinatorial effect is coming into play.”

Even though the accuracy and the efficacy of the NLP algorithm could use some improvement, it’s a great start, and it markedly accelerates the traditionally slow process of sequencing viral genomes and studying any mutations in a lab. Whereas the latter can take weeks, the former takes only a few hours. According to Bryan Bryson, a biologist at MIT who also worked on the research, “It’s wild to be simultaneously updating your model and then running to the lab to test it in experiments. This is the very best of computational biology.”

Bryson says this is just the beginning of how we apply NLP to biology, adding that analogies can go a long way in this field. As an example, Hie says that the researchers’ approach can be used in drug resistance. A bacterial protein that’s resistant to an antibiotic or a cancer protein that has become resistant to chemotherapy can be studied with NLP. Hie says that in biology, “there’s a lot of creative ways we can start interpreting language models.”

The Future of Biology

As we find more and more areas of biology to apply emerging technologies like AI, NLP, and machine learning to, we will be able to put decades of data to work for us. Analysis time will be cut down drastically, allowing us to spend more time in preparing, understanding, and making connections. The research from MIT has opened up a new way of thinking for researchers to apply NLP to analogies between language and biology, which is invaluable itself.

And Bryson, Berger, and Hie are confident that biology won’t just take from NLP, it’ll give back: NLP algorithms may one day be inspired by biological concepts and systems.

Machine learning applications like trading bots are here to save us from our despair, stress, and last-minute impulses. These bots are emotionally-neutral and infallible, working toward the investor’s goal and taking into account risk tolerance and the current market. Some finance experts believe they’re the future of the industry, and they might be right.

Qualitative vs Quantitative Approach

Coming up with a trading strategy with specific price targets and entry and exit opportunities requires a lot of research and analysis. There are two approaches to investment strategy: qualitative and quantitative. In the qualitative method, the analyst researches the company’s stock looking for several key indicators of market performance: earnings reports, company management structure and turnover, competitive advantage, planned product roll-outs, and more.

In the quantitative approach, the analyst applies statistical modeling methods to the stock’s historical data, like its performance and volatility. According to the 2020 Crypto Hedge Fund Report released by London-based companies PwC and Elwood Asset Management, the quantitative method is favored by crypto fund managers. 48% of managers surveyed say they use a quantitative methodology in their investment strategy.

Financial experts say that it’s obvious why the quantitative approach is so popular: it removes cognitive biases, emotional trades, and reactive buying and selling. In the crypto market, volatility is the name of the game, and emotional trading can cause fast money loss. The crypto market is very data-centric, allowing traders to follow key measurement points like transaction volumes, market caps, fees, and more. With these extra data points, quantitative analysts can bolster their calculations and predictions more than would be possible with traditional stocks.

Fighting Cognitive Bias

To make objective trades, it’s imperative to remove human emotion. Cognitive bias affects analysts at every level, from amateurs to seasoned veterans. Many studies have researched the influence of cognitive bias in stock trading, and many studies have looked into solutions to overcome it.

According to a field called behavioral finance, psychology influences trading so much that it is the reason for market irregularities like crashes and fluctuations. In a study by the MIT Sloan School of Management, researchers investigated how emotional reactivity affects trading performance. The report shows that, during times of crisis and volatility, extreme emotional responses can be highly detrimental to an investor’s returns.

Conversely, there is a school of thought that directly opposes behavioral finance. It’s known as modern portfolio theory (MPT), and it says that the market is efficient and traders are rational. In reality, neither of the two perspectives is 100% correct or incorrect. The truth of the matter is that both theories are complementary and in balance at any given time.

One advantage of the MPT approach is that it can be used to develop artificial intelligence (AI) applications to avoid behavioral and cognitive bias. After all, AI doesn’t have any emotion (yet). Using MPT principles, the AI can diversify a portfolio with uncorrelated assets, maximize returns, and evade loss aversion bias (favoring the avoidance of losses over potential gains).

Robots vs Humans

Trading bots are similar to humans; they come in both analyst and adviser roles, and they often apply quantitative analysis with diversification to reach the investor’s goal. Based on the investor’s risk profile, the robo adviser will create a dataset of actionable data, whereas the robo analyst will dive into SEC filings and annual reports.

Either way, the robot avoids the cognitive bias, emotional reactions, stress, and pressure that all work to bring down human analysts and advisers. The result is a robot that is proven to outperform humans. For example, in December 2019, a research group from Indiana University analyzed more than 76,000 research reports issued by robo analysts and spanning 15 years. The researchers found that the robots’ buy recommendations outperformed human recommendations, resulting in 5% higher profit margins.

This is not to say that all robots outperform humans. In 2020, researchers analyzed the performance of 20 German B2C robo advisers’ recommendations from May 2019 to March 2020. The timeframe encompassed the bull market (stable, increasing) of 2019 with a sharp drop from the coronavirus. The robots had highly disparate recommendations, resulting in a wild range of performance. The top robo adviser outperformed the others by 14 basis points on average, which is still impressive considering March caused hedge funds to lose 9.8% profit year-to-date.

Increasing Popularity of Robots

The top-performing German robot won out because of its strategic approach, quantitative analysis, and including a factor of behavioral finance. It read the market and measured what traders fear: losing money and the length of time to recover from the loss. This performance has turned major banks onto automated research robots.

Goldman Sachs announced in 2019 that it will offer its own robo adviser service. Although the launch was delayed to 2021 due to the coronavirus, the market for robo advisers has grown tremendously in that time. From Q4 2019 to Q1 2020, robo adviser usage increased between 50% and 300%.

The Future of Finance

It’s not known how far we’ll take robo analysis and advising in investing. Whether or not they fully replace human financial analysts remains to be seen. But we do know one thing: robo analysts and robo advisers excel in the data-rich and high-risk environment of the crypto market. And that means that trading robots are here to stay.

Artificial Intelligence is a technology that leverages computers and machines to mimic human brain and capabilities. AI that learns from the internet could answer a question you ask to it. Or, using a language model like GPT-3, AI can create well constructed sentences like a human being or even write passable rhyming poetry by drawing inspiration from the vast collection of human culture. On the other hand, newer generation AIs such as GPT-4 has the technical capacity to read humanity’s digitized books, all of our digitized scientific papers, and much of the blog sphere.

But, What Happens When AI Has Read Everything?

Diffbot’s AI reads every single web page on the Internet, including foreign-language pages, which means reading hundreds of billions, if not trillion, words from whether wikipedia articles, a scientific paper published by researches with domain expertise, or any other human created text. ! It then extracts as much information and facts as it can from its reading. By analyzing all the surviving text, Diffbot takes what the AI read and turns it into a number of three-part data points that can help make more connections: object, subject, and verb.

So, AI can read everything on the Internet but the infinite supply of online information grows and changes continuously. Meaning, the AI’s development will need to catch up with the ever changing and increasing knowledge on the Internet to update its data accordingly.

Synthesizing Information with Artificial Intelligence

Each three-part data point gets added to the existing knowledge base, comprised of billions of three-part data points. The data points become part of an interconnected network of information, called a knowledge graph. Knowledge graphs aren’t a new innovation; they’ve been around since the early days of AI research, but they’ve mostly been done by hand.

You’ve seen knowledge graphs in Google search results. They help you make connections by bringing together information about what you’ve searched for. For example, looking up a movie shows the cast, films related to the search, box office information, a general summary of the movie, and photos from scenes in the movie. This information is available all over the Internet by itself, but when it’s aggregated, it brings creates more value and improves mental connections for the user.

The creator of the worldwide web, Tim Berners-Lee, wanted to create something called “the semantic web,” which would’ve ultimately contained information for humans as well as machines. This vision would have allowed bots to shop for us, book our flights, and give more knowledgeable and actionable answers to questions than search engines currently do. But the knowledge graph was too cumbersome to figure out by hand, so we haven’t seen the realization of the semantic web — yet.

Google’s knowledge graph is only available for the most popular search terms. But Diffbot’s AI is a promising step towards the semantic web because it wants to generate an enormous knowledge graph for everything (not just popular search terms). After reading and analyzing unstructured data such as text documents, images, videos, social media posts etc., Diffbot fully automates the construction of the knowledge graph, and this saves the company a lot of time and manual effort while enabling the knowledge graph to proliferate at astonishing rates. Not only that, but Diffbot is only one of three U.S.-based companies to crawl the entire web, alongside Google and Microsoft.

Victoria Lin is a research scientist at San Francisco-based Salesforce. She works on knowledge representation and natural language processing (NLP). She says that crawling the web is an excellent way to automate generating a large knowledge base because otherwise, it would take a lot of human effort.

More Equipped than Humans with Advanced Machine Learning

To accomplish its job, the Diffbot AI uses a super-charged version of the Google Chrome browser to view raw pixels on a webpage. It then uses an image recognition algorithm to categorize the page into one of 20 types: discussion thread, event, article, image, and video name a few. To begin reading the webpage itself, the Diffbot AI identifies and categorizes specific, key elements on the page, like paragraphs, headings, author byline, product price, description, author bio, and more, using NLP to extract facts. Thanks to the state of the art deep learning neural networks, advanced version of machine learning algorithms, This is all done extremely rapidly, especially compared to a human.

When a three-part data point is generated, it gets added to the ever-growing knowledge graph. It doesn’t matter if the language doesn’t align with the user’s query; if a user asked about a specific movie, it’ll pull information from articles written in Hindi and Mandarin. The CEO of Diffbot, Mike Tung, says watching the AI read web pages is like watching someone play a video game. It must navigate around pop-ups, between tabs, and scrolling through pages.

Diffbot’s knowledge graph is rebuilt every four or five days, but the AI reading bot is crawling the web non-stop. It adds 100 to 150 million new three-part data points to the knowledge graph every month as companies, people, and products get added to the web. It uses machine learning applications to connect new facts with old ones, which sometimes requires rewriting out-of-date facts or simply fusing the new with the old. As the knowledge graph continues expanding rapidly, Diffbot has faced intermittent challenges with maintaining enough server space for training data and processing power in its data centers.

Limitless Industry Applications with Deep Learning

Diffbot allows researchers access to the knowledge graph for free. But the company also boasts an extensive portfolio of 400 paying customers, from DuckDuckGo (uses Diffbot to generate Google-like knowledge graphs), Snapchat (uses Diffbot to rapidly extract highlights from news articles), NASDAQ (uses Diffbot to get fast information about the stock market for financial research), and Zola (uses Diffbot to help brides and grooms make wedding lists by pulling in prices and images).

Even Adidas and Nike use Diffbot to scour the web for counterfeit shoes on sale. While Adidas could simply search in Google for articles mentioning “Adidas trainers,” Diffbot goes the extra mile by letting the company look for sites that have products that mention “Adidas trainers,” so all effort can be put towards sites actually selling “Adidas trainers” products.

Diffbot’s Next Move

Companies using Diffbot’s knowledge graph have to interact with it using code. But, eventually, Tung wants to add an NLP interface to create an application that allows users to ask almost anything and get a response from the Diffbot AI with sources attributed. Dubbed a “universal factoid question answering system,” Tung says this won’t be possible with only NLP, but it’s an option if you combine multiple technologies.

But, according to Tung, Diffbot isn’t out to define intelligence. Instead, the company is “just trying to build something useful.” What do you think of Diffbot’s AI? Would it be useful for consumers, in addition to commercial uses by various businesses?

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When we think of artificial intelligence (AI)-enabled cars, most of us jump straight to self-driving cars that take away the most difficult parts of driving: the anxiety, the constant attention required, and avoiding a collision. But an AI application in the form of a road rage chatbot may end up having the most profound effect on our driving.

The Great Expectations for Autonomous Driving Are Unfounded So Far

Before the pandemic, according to the U.S. Census, three out of four U.S. workers were commuting to their office in a car. From 2005 to 2017, there was a 32% increase in “super commuters” that trekked 90 minutes or more one way. Driving such a long time every day inevitably leads to exhaustion, which messes with our general perception and emotions. In general, longer driving commutes have been linked to high blood pressure, stroke, obesity, and sleep disorders.

Switching from driving is one solution to find some relief. Research shows that those who take trains, walk, or bike to work tend to be happier commuters than driving commuters. A University of Amsterdam study even found out that those taking alternative methods to work miss their commute more during lockdown than those who usually drive to work.

But not every town or city has the infrastructure of New York City or San Francisco; taking a train or bus could add hours to a person’s commute, especially as the suburbs expand out to offer more affordable housing. Even in a big city like New York City, there are very few concessions for disabled or wheelchair-bound commuters, and regular train commuters often face delays and closed subway stations without prior notice.

Automotive manufacturers have touted the idea of self-driving cars for the past few years. The companies claim that this cure-all technology will be available in the near future, and it will allow us to relax while the car, equipped with machine learning applications and an array of sensors, drives us safely to our destination. Eventually, we’ll be able to sleep, read a book, or watch Netflix without worrying about causing an accident or missing the right highway exit.

Google said in 2012 that self-driving cars would be widely available within five years, and they repeated this sentiment again in 2015. Tesla hasn’t launched any fully autonomous cars either. It’s almost the end of 2020, and we’re mostly still relegated to our old-school cars.

It turns out that it’ll actually be decades before fully autonomous cars hit the road in mass numbers, so this is not a solution available for daily commuters and road rage incidents. We need to work on creating a solution that doesn’t require buying a brand new car (how many people can afford to hand over $40k+ for a brand new car in the next five years, anyway?) or installing an expensive after-market technology.

Research to the Rescue

A small group of researchers is tackling how we can make our cars work for us and make us happier while we drive. The most stress-inducing driving incidents occur when drivers need to change lanes, enter a crowded and complex intersection, and make left and right turns. Car manufacturers have started including more advanced technology to increase driver safety, like blind spot sensors, collision detection, and drowsy driving detection.

Digital transformation Trends in automotive industry. Smart car , Autonomous self-driving mode vehicle on metro city road iot concept with graphic sensor radar signal system , internet sensor.

A 2015 study found that driver commuters were more stressed by their trip than those taking public transportation or biking to work. The drivers mentioned that it was because of the inconsistency that traffic, accidents, roadwork, and other traffic issues create in their schedule and commute. An Oregon-based company named Traffic Technology Services (TTS) develops a product called the Personal Signal Assistant.

It lets cars communicate with traffic signals in towns where that data is publicly available. Audi, TTS’s first client, used the system to add a tool to their cars that visually counts down how much time is left at a red light for the driver. This tool was originally designed to keep traffic flowing nicely, but Audi drivers reported a huge decrease in stress.

Enter a Friendly Little Chatbot

Pablo Paredes is the director of the Pervasive Wellbeing Technology Lab at Stanford’s School of Medicine. His lab focuses on how to change the habits and objects that people use in their daily lives to improve their physical and mental health. For Paredes, the daily commute is a challenge he is excited about transforming into something more therapeutic. “There are very simple things that we’re overlooking in normal life that can be greatly improved and really repurposed to help a lot of people,” he says.

In a 2018 study, Paredes and his team created a technology to infer the driver’s muscle tension using their hand movement on the car’s steering wheel. They’re continuing to improve the technology to this day. The lab uses a Nissan Leaf that’s been equipped with a variety of technologies from years of work and experimentation at Paredes’s lab. These tools are all designed to work together to decrease the driver’s stress.

One of the newer technologies is a chatbot that offers guided breathing exercises. It involves using the driver’s seat to vibrate along the driver’s back to create a rhythm for the driver to inhale and exhale to. The study’s results showed that the breathing exercises reduced driver stress and breathing rate without impairing the driver’s performance or attention. The next step for this tool is to use lower-frequency vibrations to slow breathing and reduce stress without the driver’s conscious attention.

The lab wants to eventually sell a car with these tools outfitted within the automobile’s system: the chatbot would even make a joke or talk through a problem with you, guided by learnings from cognitive behavioral therapy research. Paredes says this technology can fit right into a fully autonomous car when the time comes because the person inside will still be stressed, anxious, and fearful.

ETA Is Unknown

Paredes’s lab suspended research during the pandemic since it’s difficult to socially distance in a compact car. Although the lab has filed patents for the technologies, it’s not known when the tools will be released for the public to purchase. TTS is expanding its technologies with other auto manufacturers, but they will likely be released in only new cars.

And, of course, there are data implications, as well as ethical considerations for commercial drivers (should their companies be allowed to mandate that the driver not use the relaxation technology due to the duties of the job itself?). Will use of the relaxation technology be available for law enforcement to use against you in court if something happened? Overall, however, these are the same questions we’re asking about fully autonomous cars, and the answers will vary by the type of technology as well as its application.

What do you think about adding relaxation features to your car? Would it improve the driving experience for you, or would you rather wait for fully autonomous cars to launch? Let us know in the comments below!

There’s no shortage of hype surrounding 5G. Per the June 2019 Ericsson Mobility Report, “No previous generation of mobile technology has had the potential to drive economic growth to the extent that 5G promises.” Now, this potential is finally becoming a reality. 5G is poised to disrupt practically every industry. But the fifth generation of cellular technology may not be here as soon as you think. A few obstacles stand in the way of 5G’s arrival. Still, opportunities abound for ambitious entrepreneurs.

How 5G Will Usher in a New Era for Numerous Industries

Qualcomm claims 5G will reach initial download speeds of 1.4 gigabits per second. That’s about 20 times faster than 4G. Latency, the lag you experience when issuing a command to your smartphone, is also important to consider. A lag of a few hundred milliseconds is common with 4G. 5G will trim this down to a couple of milliseconds, making data transferring much more reliable.

This unprecedented speed and reliability will change the landscape of technology. 5G will open up a multitude of avenues for developers to build new mobile app features. It will also enable new capabilities in emerging technologies such as artificial intelligence (AI) and the Internet of Things (IoT).

By allowing algorithms to expand their training datasets and run analyses concurrently, 5G will foster faster innovation and growth for AI and machine learning applications. The enriched insights and heightened precision that this produces will immensely improve the orchestra of sensors that IoT relies on.

Various sectors will benefit from these advantages. Smart cities and connected autonomous vehicles will become more viable. Virtual reality goggles will dwindle in size to resemble the devices we’ve seen in science fiction. We’ll be able to experience augmented reality in real-time. And manufacturing supply chains around the world will be able to optimize every step in their process.

5G will also transform healthcare applications. MRI machines and blood pressure monitors will be able to transfer data without delay. Surgeons will be able to operate remotely with no latency between their movements and those of a corresponding robotic arm. 5G will also save you a trip to the doctor’shttps://www.dogtownmedia.com/app-development-services/healthcare-app-developer/ office; telemedicine is expected to grow at an annual compound rate of 16.5% from 2017 to 2023, thanks to this technology. All of this adds up to better experiences and outcomes for patients.

Tempering Expectations

To reach the reality described above, we must address a few substantial challenges. Building 5G applications simply isn’t practical yet. Only the most prominent telecom companies have access to 5G kits, and only a handful of phones are 5G-enabled. In fact, 44% of telecom companies that responded to a survey by JABIL don’t have the proper tools for testing and managing 5G applications.

In a survey of 204 executives working at telecommunications companies with over 1,000 employees working on 5G network development, Jabil found that 53% of respondents believed 5G’s sheer complexity would be the greatest challenge to overcome. This problem becomes more convoluted when you consider that mobile carriers are providing different iterations of 5G as they try to capture market share as quickly as possible. Consequently, developers can’t define what 5G actually is; we’re stuck playing a guessing game.

To make matters worse, the vast majority of developers don’t have access to 5G infrastructure. Besides new software and devices, 5G requires hundreds of thousands of cell sites. Installing this infrastructure easily amounts to hundreds of billions of dollars in costs.

So when should we really expect 5G to become mainstream? According to 60% of Jabil’s survey participants, that should happen by 2021. 20% think it will occur in 2022. And 11% are patiently waiting until 2023 for 5G to become ubiquitous.

Fortunately, there is a silver lining to all of this.

How Entrepreneurs Can Get Ready for 5G

Regardless of when 5G arrives, entrepreneurs can take steps now to prepare for this new mobile era:

1. Practice with Simulations

You may not have access to true 5G network capabilities. But that doesn’t mean you can’t replicate them. For instance, Dogtown Media, my mobile technology firm, has used tools like the Raspberry Pi device and DIY kits such as Framework to simulate 5G’s rapid data transfer capabilities in a development environment. Beyond this, Qualcomm and some telecom consulting companies now offer services that enable developers to simulate 5G experiences.

These substitutes are usually much slower than the real thing. But they still give you a great way to practice.

2. Plan for Two User Experiences

5G adoption will be piecemeal. Major telecoms aren’t incentivized to make the costly infrastructure investments necessary to bring this technology to rural areas immediately. So a large amount of the U.S. population will have to make do with slower networks.

To bridge this digital divide, development teams should plan on creating 4G and 5G experiences of their products. By tailoring your offerings to this reality, you can capture more of the market share.

3. Brace Your Budget for More Work

All of this extra work to make two user experiences requires more time, effort, and money. So plan ahead accordingly by increasing your development budgets by 20% to 50%.

Users who travel across coverage areas may instantly downgrade or upgrade to different networks as a result. To accommodate this and provide a seamless experience, technologies must be flexible and capable of automatically detecting connectivity. This creates even more work for your development team.

5G has tremendous potential to create new value chains, unlock new business opportunities, and fundamentally change how we interact with technology. There’s no doubt it will bring radical transformation. But getting to this point still requires significant investment and serious work.

Don’t let this dissuade you from preparing for this new mobile era. While many people are patiently waiting for this future to come, others are working on making it a reality. At Dogtown Media, we’re creating the mobile future every day. Get in touch with my team for a Free Mobile App Consultation. If you can dream it, we can build it!

Artificial intelligence (AI) and machine learning are allowing both businesses and consumers to boost their cybersecurity to unprecedented levels. In a recent post, we examined six ways that AI is leading the way towards rock-solid information security. In case you missed it, read it here.

For this article, we’ll take a closer look at how AI and machine learning are letting three major industries safeguard their data better. In each of these sectors, websites not only contain a wealth of sensitive information but also have a high volume of visitors every day. Let’s explore how AI helps to counter any threats that come their way.

1. Finding Fraud and Anomalies in Finance

As its name implies, anomaly detection is a technique that leverages AI development to identify any unusual activity. A prime example of this would be if a bank customer unexpectedly withdraws an enormous amount of money from his or her account. Because this goes beyond the customer’s usual behaviors, AI would flag this action and alert both the bank and account owner.

Credit card fraud happens to be one of the biggest problems for financial institutions. To minimize these threats, AI utilizes a misuse identification technique. In much the same way as our previous example, potential fraud is identified when transactions fall outside the boundaries of previously established customer behavior. For instance, if you live in New York City and your credit card was used to purchase something expensive in Beijing, then this would raise some alerts.

Loan application fraud is another substantial issue in the banking sector. To keep the loan application process efficient, convenient, and secure, banks employ AI to rapidly analyze an applicant’s information, identify any anomalies, and verify authenticity. The smart technology plays an integral role in eradicating fraudulent applications early in the process so that more resources can be dedicated to legitimate customers.

2. Ensuring Accuracy in Insurance

With the immense amount of information that insurers collect about individuals and organizations, it’s no wonder that they’ve become an incredibly valuable target for hackers. To remain competitive in today’s fast-paced world, many insurance firms have digitized their products and pivoted to online platforms. Unfortunately, this shift has sparked the possibility for new security threats.

As in banking, fraud is common in insurance. A noticeable portion of prospective policyholders fabricate information in order to manipulate the rates they’ll receive from insurance firms. Similar to finance, AI plays a vital part in inspecting and validating the data submitted by customers.

AI and machine learning applications are constantly updated to be familiarized with upcoming fraud trends. They’re also optimized for honest customers, too. Besides flagging potentially false claims for further investigation, AI also automatically detects and validates legitimate claims. In turn, this streamlines the processes of approval and payment. As a result, insurance companies can lower their costs, and customers receive reduced prices.

3. Keeping Personal Information Private in Healthcare

Since thousands of employees can now digitally access patient information, privacy and data protection in healthcare has become quite complex. Obviously, manual evaluation of patient data interactions is infeasible; it would take far too much time and energy. Luckily, AI is here to help.

AI-powered medical applications can meticulously scan all patient data transactions and assess the various factors related to each interaction in just a few seconds. This takes into account the area of access, number of times accessed, and the length of time each login remained active. So if a staff member’s account suspiciously accessed thousands of patient files within a minute, AI would definitely pick up on this unusual behavior.

Beyond electronic medical records, AI is also helping to make healthcare devices safer. An array of gadgets like insulin pumps and pacemakers are susceptible to cyber-attacks. But these are used by many people around the world. In a worst-case scenario, a pacemaker could even be directed to shock the patient.

Like in banking and insurance, AI’s anomaly detection prowess comes into play here. The technology can track all activity and identify any abnormal instructions being sent to the pacemaker system. In this case, AI literally makes the difference between life and death.

AI Is Opening up a New Era for Cybersecurity

Believe it or not, this is just the beginning of AI’s use in cybersecurity. Companies in different industries are the world are just starting to understand the benefits of incorporating this technology in their fold. Even tech titans like Google, Amazon, and Microsoft are still in the midst of shifting away from rule-based protocols to machine learning algorithm deployments that can analyze vast quantities of information.

With that said, hackers are incredibly resilient; there’s no doubt that some are already leveraging the capabilities of AI and machine learning for their nefarious acts. It’s imperative to remember that cybersecurity isn’t a result — it’s a process. Adopting new paradigms, updating old protocols, and staying abreast of developments is key to properly protecting your information.

How do you ensure your cybersecurity is rock-solid? Are you utilizing AI in these endeavors? If so, how? Do you think that AI will ultimately be good or bad for cybersecurity? As always, please let us know your thoughts in the comments below!

By now, we’re all aware that the development of artificial intelligence (AI) and machine learning will shape our future in several ways. But many of us do not know how these technologies will impact cybersecurity.

We find ourselves at a pivotal moment in this digital era — one in which our personal information is at unprecedented risk. The last decade alone was riddled with hundreds of massive data breaches and identity fraud incidents. Today, cyber criminals can achieve their objectives from anywhere in the world, at any time.

Our need for more progressive cybersecurity measures has never been more imperative than now. Fortunately, cybersecurity applications have received numerous technological advancements over the last few years. Chief among these game-changing developments is the introduction of AI and machine learning to the field. Let’s examine how these technologies are augmenting current cybersecurity endeavors.

1. Improving Cyber Threat Detection With Machine Learning

In cybersecurity, foresight is priceless. Detecting cyber attacks in advance can give organizations the time they need to successfully neutralize these incoming threats. And it turns out that the application of machine learning to data analysis can help immensely in identifying them.

Machine learning helps computers learn from and understand obtained data. In turn, systems can adjust and refine algorithms to reach optimal performance. In terms of cybersecurity, this means that machine learning can enable computers to detect threats and anomalies more accurately than any human is capable of doing.

Traditional technology relies too much upon previous results. Often, this leaves it unable to adapt fast enough to hackers’ latest techniques and strategies. And the sheer volume of cyber threats that people face every day is too immense for human-directed systems. On the other hand, AI allows computers to excel at improvisation by adapting faster than ever before.

2. AI-Fueled Phishing Detection and Prevention

Phishing is the fraudulent practice of sending fake messages. Hackers use this all the time; they pretend to be from reputable organizations or groups so that victims either reveal personal information like passwords or install malware. Phishing emails are so common that one in every 99 email messages is believed to be an attempted attack.

Luckily, AI and machine learning play an integral role in mitigating phishing attacks. Besides being able to respond much faster than a human can, these technologies can identify and track over 10,000 active phishing sources. They also allow for swift distinction between fake and valid websites. Because these technologies are now being employed around the world, AI’s knowledge of phishing campaigns isn’t relegated to only one geographic location.

3. Making Vulnerability Management Easier

Every modern business relies on information technology (IT). But keeping your IT safe can be difficult. Just this year, over 2,000 unique cybersecurity vulnerabilities have been recorded. Managing these with only humans would be practically impossible. Thankfully, AI opens up an easier approach.

AI- and machine learning-based systems can efficiently scan for potential flaws in corporate IT systems. And, by incorporating recent relevant information such as dark web forums, hacking trends, and more, these technologies make it simple to stay on top of the latest developments in this field. With all of these insights, you’ll not only know how your vulnerable targets may be attacked, but also when.

4. More Powerful Password Protection and Authentication

Passwords have always been one of the weakest components of security control. In fact, they’re often the only link between cyber criminal activity and our identities. Biometric authentication is seen as a potential alternative for the future, but it’s currently not the most convenient paradigm to employ. AI could change this.

Developers are leveraging AI to improve biometric authentication and eliminate any weaknesses so that it’s more robust. Apple’s facial recognition technology is a prime example of this. Known as Face ID, this system detects a user’s facial features via infrared sensors. Apple’s AI software then produces a sophisticated representation of the user’s face that allows it to recognize key similarities.

Apple is so confident in this technology that it believes hackers have a one-in-a-million probability of bypassing it. This system also works under different lighting conditions and can compensate for changes such as a new hairstyle or more facial hair.

5. Automated Network Security

Security policy development and organization network topography are two essential components of network security. Unfortunately, both take up a monumental amount of time and human effort to fulfill and manage.

Fortunately, AI can automate both of these processes to some degree. By analyzing network traffic dynamics, AI can generate and recommend policies and procedures to fit your unique situation. The amount of time, energy, and money this could save organizations can’t be overstated.

6. More Robust Behavioral Analytics

Similar to our other examples, AI and machine learning can also be employed to improve behavioral analytics by studying your patterns. This allows them to understand how you use your computer and other smart devices. Details can include but are not limited to your favorite online platforms, usual login times, as well as your texting and browsing patterns.

If an algorithm detects unusual actions that are outside your normal patterns, it can lock the culprit of this questionable activity out of your system. Massive shopping sprees, shipping products to addresses other than your own (e.g., why’d you ship that new game console to Beijing if you live in Los Angeles?), a sharp spike in uploads or downloads of files, and even a change in your typing pace can all alert AI to nefarious behavior.

AI and ML Make Smarter Cybersecurity Possible

We hope you’ve enjoyed this list of amazing ways that AI and machine learning are improving cybersecurity. As far as security goes, these emerging technologies have vast potential for sectors such as finance, retail, and healthcare.

Speaking of different industries, stay tuned for our follow-up post to this article! We’ll delve into how AI is preventing data breaches in three large sectors.

In the meantime, what do you think of AI and cybersecurity together? And what cybersecurity measures do you employ to protect your information? As always, let us know your thoughts in the comments below!

The progression of technology and scientific knowledge go hand-in-hand. We now live in an era of constant advancement in both fields. Fueled by unquenchable curiosity and machine-powered efficiency, we’re pushing the boundaries of what we know with every passing day.

Breakthroughs such as curing ailments with gene editing, bioprinting human organs, and making preventative medicine a reality are just some of the topics that consistently take over headlines. Each of these developments has the ability to significantly improve our lives.

But the unfortunate truth is that current paradigms do not support the optimization of research potential. As a result, discovery yield is skewed; some findings hold merit, while many others fall to the wayside after their promise fades. Numerous research papers are retracted while few endeavors go on to shape a better future for society.

Luckily, the Internet of Things (IoT) can address this. Implementing IoT development in the laboratory environment can bring a new level of efficiency to research and development (R&D). In the future, IoT’s data collection and automation capabilities will usher in the arrival of the smart lab.

The Issues With the Status Quo

Numerous barriers in laboratories obstruct scientific progress. They come in a variety of forms; human errors, lack of compliance, device malfunctions, and miscommunication name only a few.

Perhaps one of the biggest problems is manual recording of machine data output. This repetitive and time-intensive task is ripe for human mistakes to occur. Besides this possibility, data loss, an issue when a scientist is too selective about what to record, can also arise. Sometimes, researchers can’t decipher the information at hand or even the handwritten notes of their colleagues.

All of these issues mentioned do have one thing in common: a lack of connectivity, both between researchers and the equipment they employ. And if you’re an avid reader of our blog, then you know that nothing solves connectivity problems like IoT.

How IoT Overcomes These Obstacles

IoT can bring about a more effective, efficient way of conducting research experiments and collecting the resulting data. This technology can facilitate the connection of every element in the laboratory, from scales to centrifuges and everything in between. Machine output can be digitally transmitted via digital format, saving scientists hours of time and effort as well as eliminating the chance of human error.

With IoT, labs can connect all devices to the cloud or local server. This enables researchers to access and control experiments and processes anywhere, anytime (as long as they have an internet connection). If a scientist from the Bay area is visiting Boston, for example, then they can check in on their operations and developments in San Francisco remotely to ensure that everything’s running smoothly.

In the laboratory, IoT can take on various forms. Automation will be one of the most common examples. Automating all lab equipment, even down to material containers, can unlock unparalleled productivity. Currently, this is an expensive route to go, leaving it only available to successful industry lab facilities. As with other technologies, the price should diminish substantially in the future.

IoT Adoption Is on the Rise

Despite steep costs, the adoption of IoT in the laboratory setting has seen an abundant increase in demand. This is most readily apparent in industrial R&D; the need to compete in the global market means the benefits of IoT easily offset any expensive price tag.

Alongside this demand growth, our society in general has pivoted more towards digital solutions and an emphasis on easier access to data in recent years. And, as in consumer markets, IoT implementation brings numerous advantages to R&D, such as seamless experiment execution, more accurate data documentation, and more accessible research findings.

It’s no surprise that those working in R&D want to experience the same convergence of our digital and physical worlds that consumers around the world are now privy to. With that said, IoT will eventually give rise to the smart lab.

The Future of Laboratories

Everyone has heard of smart homes and smart cities. Both are made possible by a mixture of IoT and artificial intelligence (AI). Similar to these concepts, the smart lab simply refers to connecting all laboratory machines and sensors to the internet.

By controlling all of these devices externally, researchers can execute experiments with unprecedented speed and precision. The application of machine learning (ML) and AI technology further streamlines these benefits and enables far easier data documentation than what researchers contend with today. Connecting every lab tool in this way allows for a smart environment where machines can both predict experiment outcomes and produce hypotheses based on these findings.

With a drastic reduction in the amount of human intervention required, researchers will be freer to dedicate time to more important initiatives. And as all data is stored in the cloud, they can rest assured knowing no research will be lost. Collectively, these advantages will accelerate scientific development.

A Smarter, More Connected Future for Science

With a strong emphasis on efficiency, compliance, and precision, the laboratory environment is the perfect place for IoT integration. IoT-enabled devices could considerably increase both productivity and discovery yield.

Science is governed by a stringent set of principles. Yet, today, many researchers struggle with ensuring their work is FAIR ( findable, accessible, interoperable, and replicable). IoT and smart labs will change this by ushering in a new era for R&D — one that will continue to reshape and improve research for years to come.

What do you think of IoT’s future in R&D? Do you think there are any valid concerns about implementing this technology in this field? Or do the pros greatly outweigh the cons? As always, let us know your thoughts in the comments below!