As the 2021 year comes to a close and we anticipate what’s to come in 2022, it’s with great appreciation and honor to announce that Dogtown Media has received a global accolade from the major digital rating agency, Clutch.co, as a Top Global B2B Company for 2021.

After 10 years in the mobile app space, reaching a highly regarded and recognized global accolade is a major accomplishment and points to the continued dedication of Dogtown Media to their global client base and their hyper-focus on producing high-quality applications. 

Dogtown Media is Los Angeles’ leading mobile application company, working with organizations in nearly every vertical to bring their unique ideas and solutions to the app market. Dogtown Media prides itself on the satisfaction, approval, and happiness of our clients. And aims to create cutting-edge solutions that are pushing the boundaries of what’s thought o be possible in the mobile application space.

the mobile application space.

And for those who may be unaware, this Clutch.co accolade is only one in a series of major accolades awarded to Dogtown Media such as Top 2021 B2B Leader in Artificial Intelligence for Robotics, a top 2020 Service Provider, and the 27th Best B2B Service Provider in the World in 2019. All of these great accolades point to the dedication to craft and customer, and only scratch the surface of their long laundry list of accolades from Clutch and other prominent rating agencies in the mobile app space. 

“This recognition feels surreal and we are lost for words”, notes founder Marc Fischer. “We feel truly honored to be recognized by such a prestigious rating firm, and hope to continue to provide high-quality, meaningful applications for our clients today and far into the future. “

Here are some of the quotes that stood out most to us:

Here are some of the quotes that stood out most to us:

“They were an effective team, met deadlines, and created a great end product.“. — Director, Risk Comm Lab, Temple University

“They built an intuitive and simple design, and the team works quickly to address bugs and solve problems.”— Senior Ops Manager, Hospital Innovation Lab

Let’s build something amazing together! Connect with us and get a free tech consultation.

A new artificial intelligence (AI) algorithm is testing just how important attractiveness is by attempting to figure out who you’ll find attractive and why you find that person attractive. A team comprised of researchers from the University of Helsinki and Copenhagen University generated images of fake faces that they then asked people to rate for attractiveness. It then used that feedback to further tune the AI algorithm, making it perform even better at generating fake attractive faces.

Adversarial Strengths

The application, which uses a machine learning development algorithm called generative adversarial network (GAN), creates fake faces by pitting two “adversarial” algorithms against one another. Adversarial means the two algorithms have differing goals: one, called the generator, creates images based on what it learned during its training phase, and the other (called the discriminator) tries to figure out which of the generated images are fake or real. The latter algorithm is tested with photos of real people and fake faces.

The two algorithms eventually start training each other in a loop that allows the generator and discriminator to improve their performance greatly with each cycle. The generator improves its ability to create realistic images, and the discriminator gets better at finding fake faces. This symbiotic relationship may first sound unproductive, but the algorithms work well together and also successfully test each other.

The GAN algorithm was trained on 200,000 images of celebrities, who usually have attractive faces—at least, according to Hollywood standards.

Testing Attractiveness

After the training phase, the generative algorithm developed hundreds of unique faces of people who it believed were of similar attractiveness as the celebrities it “knew” to be attractive. These fake faces were shown to real people who wore brain-computer interfacing equipment hooked up to an EEG (electroencephalography) reader. Using this data, the researchers could measure each person’s brain activity for each photo they saw, down to the neuron’s exact moment of firing.

When a participant saw an image of an attractive face, there was a marked increase in brain activity. This could be partially attributed to the fact that the participants were told to focus harder on faces they thought were attractive. The participants weren’t asked to articulate what specifically they found attractive about any of the images. Instead, the AI stored the EEG datapoints and found the commonalities within each photo.

Those commonalities could be big eyes, high cheekbones, a medium-sized nose, wide-set eyes, small ears, or any other facial feature. The AI found that most participants liked the same aspects of a face in an image. In other words, humans seem to favor most of the same facial features when asked about attractiveness.

Using the common features found by the algorithm, the team distilled this data back down in a format that could be fed to the GAN algorithm. The generative algorithm then took this new information as instruction in making its second batch of attractive faces. Now, the faces had more chiseled jawlines, darker and more mysterious eyes, curlier hair, and more features that we find conventionally more attractive.

Real Looks vs. Fake Faces

When this second round of generated photos was shown to participants, they were instructed to rate the face as attractive or unattractive. For 87% of the newly-generated photos, participants rated the face as attractive. The remaining 13% were either too perfect or there was something that seemed off about their facial features. Even though the participants were told to focus on attractiveness, they were unable to look past how some faces looked fake or off.

AI developers and AI ethics experts worry that this type of well-performing technology could be used to generate faces that look realistic for the purposes of deepfake videos or fake images. Not only do the faces not need to be real, they don’t need to be attractive to cause issues for people or even nations. And the consequences don’t need to be so far-reaching: even social media accounts used for a malicious purpose could use AI-generated fake faces to blend in with the crowd. They might even look normal and real at a quick glance. After all, how much detail can you see in a small circular avatar?

The Future of Dating?

The future of this type of technology extends far beyond dating and social media. It could be used for political gain or even start a war. The research team is interested in advancing the technology, and it has some ideas for how to use their application in a productive and non-malicious way. Associate professor at the University of Helsinki, Tuukka Ruotsalo, says that the team hopes to dig deeper into attractiveness, as well as explore stereotypes, biases, preferences, and individual differences.

Have you come across an AI-generated face that was attractive but looked off? How did it make you feel? Let us know in the comments below!

The model simulates how a human’s brain processes information using neurons and synapses (our own “hardware”) instead of computer CPUs. Intrigued? Read on, it gets even more interesting.

Quantum Computing

Using the inherent quantum properties of cobalt atoms, a team of researchers from the Radboud University in the Netherlands created organized networks of atom spin states. With these networks, a quantum brain was developed that can process information and save it to its memory. This is no longer “artificial” intelligence; it’s the closest computer model we have to a real human brain and how it works.

It’s well-known that machine learning applications are made up of algorithms that take up a lot of energy and require a lot of data. And although Google, Apple, and Amazon have enormous data centers to overcome that limitation, it’s not a realistic situation for the hundreds of smaller AI research firms and institutions. Experts also worry that computing power may be reaching its peak, despite what Moore’s Law says about the rate of advancement of technology.

This new computing method is a promising alternative to overcome these limitations. And, according to the lead author, Dr. Alexander Khajetoorians, the new method “could be the basis for a future solution for applications in AI.”

Integrating Neuroscience

Many AI methods, like deep learning, are already modeled loosely after the human brain. But our current computing technology is limited by the fact that memory and computing units are separated from each other, creating a time, energy, and resource issue when data has to be shuffled back and forth for complex algorithms that require a lot of training data. Experts are concerned about how far we can optimize AI algorithms for efficiency with our current computing technology.

In contrast, the cobalt method allows us to store and compute in one unit. It forgoes CPUs, memory, and chips, allowing for faster computation and memory retrieval as well as less energy consumption. The cobalt method is also extremely flexible: if the algorithm learns that a new factor makes it perform better, it has the capacity to store this updated information in relation to the original for faster retrieval next time. This is incredibly similar to the brain, and it could be the future of computing technology.

Cobalt’s Quantum Spin States

The Radboud University research team has been working on this problem for years. In 2018, the group found out that a single cobalt atom could possibly unlock a computing model closer to neurons and our brains. They found out that they could use several properties of quantum spin states to make this a reality. For example, an atom can have multiple spin states at the same time, and the atom will have a certain probability that it’s in one of each state. That’s similar to how neurons decide to fire and how synapses pass on data.

Another property they dived into was quantum coupling, which involves two atoms binding together in a way that the quantum spin state of one atom influences the other to change. This is also similar to how neurons communicate.

With these two insights, the team worked on building a computing method that was modeled after neurons and synapses. They added multiple cobalt atoms onto a superconducting surface made of black phosphorus. Then they took on the challenge of figuring out if they could induce networking and firing between the cobalt atoms. They wanted to know if they could simulate a neuron firing. They investigated if it was possible to embed information in the atom’s spin states.

After working out a “yes” to those questions, the team used weak currents to send the system 0s and 1s, which could be translated into probabilities of the atoms encoding 0 or 1. Then, the team charged the atoms with a small voltage to simulate how our neurons receive electrical signals before they act (or don’t act). The result was surprising and significant. The voltage caused the atoms to behave in two different ways: it caused them to “fire” and send information to the next atom, and it changed their structure slightly afterward as we see with synapses.

Khajetoorians said, “When stimulating the material over a longer period of time with a certain voltage, we were very surprised to see that the synapses actually changed. The material adapted its reaction based on the external stimuli that it received. It learned by itself.”

A New Kind of Future

Our current computing hardware requires the dangerous mining of rare elements and materials, and using cobalt quantum states offers more ease, affordability, and efficiency. But it will still be a while before we see this innovation in our data centers and computing models. We will need to prove its ability before we see it being used in San Francisco‘s Silicon Valley.

The team must still figure out how to seamlessly scale the system and demonstrate its usage with a real algorithm. We’ll also need to develop a machine for this new technology. Although there’s still a lot of work to be done, Khajetoorians is excited for the future of his research. After all, his team may be the foundation of AI’s new future.

For example, according to Sam Altman, the co-founder and president of OpenAI, a San Francisco-based AI non-profit, AI will eventually create so much wealth that every adult in the U.S. will take part in a form of profit sharing. To be specific, Altman says that $13,500 could be paid to every adult in possibly 10 years from now. It won’t be as easy as it sounds, though: the government will need to take action and support the distribution of wealth that AI creates.

AI’s Ever-Increasing Pace

Altman believes that an AI revolution is coming sooner, rather than later. He says, “My work at OpenAI reminds me every day about the magnitude of the socio-economic change that is coming sooner than most people believe. Software that can think and learn will do more and more of the work that people now do.” Altman says that the AI revolution is of the scale and magnitude of past major periods of growth, like the industrial, agricultural, and computational revolutions.

According to Altman, the progress we make with technology in the next 100 years “will be far larger than all we’ve made since we first controlled fire and invented the wheel.” But it won’t be possible, especially the sharing of wealth generated by AI, unless the government helps. First, public policy needs to adapt to modern times and modern technologies. Second, the government must collect and redistribute the wealth that is generated. If done correctly, AI could make the future “less divisive” and more fruitful for everyone.

Some major cost savings will come from AI applications‘ ability to read legal documents or give medical advice. Further down the road, AI will take over assembly-line work and advance enough to become companions to humans. And in the decades afterward, AI will have taken over almost everything, even “making new scientific discoveries that will expand our concept of ‘everything.'”

As it advances, the price of labor will drop to zero and AI will create extreme wealth.

A Whole New World

Altman sees a bright and optimized future where, “for decades, everything – housing, education, food, clothing, etc. – [will become] half as expensive every two years.” But this vision requires a lot of fundamental changes to how taxing works and how taxes are used in the U.S. For example, the wealth from AI will come from the taxation of companies and land, not labor. In other words, governments should tax capital and distribute those taxes to citizens.

According to Altman, an “American Equity Fund” would tax companies above a certain size 2.5% of their market value in company shares. Additionally, a 2.5% tax of all land value will be levied in dollars. Private companies that generate revenues over $1 billion would also be subject to taxation and dollar payment.

Adults over the age of 18 would receive payment in dollars and company shares. How they spend it or hold onto it is up to them. This method provides each adult some ownership of the country, and it gives them an incentive to keep their country’s economy going, which would theoretically improve society and life for everyone.

With this setup, each of the 250 million American adults would receive $13,500 annually. This is an estimate using the current figures of a $50 trillion valuation of all large U.S. company market caps and a $30 trillion valuation of private land in the U.S. In the next ten years, Altman says, both of these figures will approximately double.

Altman added that the $13,500 estimation could be much higher if AI grows even faster, but that $13,500 as it still has more purchasing power in the future than it does now because the cost of goods and services will be highly reduced in the future. After a certain point in time, a consumer’s purchasing power will increase dramatically every year, says Altman.

Supporting Parties

Altman isn’t the only person with this thought about the future. Elon Musk agrees that automation created by AI will create the need for a universal basic income in the future, and he said so in 2016. Musk was a co-founder of OpenAI but left its board in 2018 when he felt that Tesla was becoming an AI company, causing a conflict of interest.

Altman says that his proposed system is both “pro-business and pro-people”, and involving both with each other will result in a harmonious relationship. But the major obstacle is the actual probability of these paramount changes happening within the next decade. The U.S.’s political climate does not seem to be going in the right direction to solve the looming and growing socioeconomic chasm.

Giving Back to Humanity

As AI and machine learning applications grow rapidly in complexity and usefulness, we need to engineer a way to pay it back to those from who we are taking jobs, training, and education. And although Altman’s plan sounds great, it seems way too idealistic.

For Altman’s plan to be successful, we need a wild shift in political leaders, policies, and social programs. We need to start working towards the future today, not in ten years. As Altman says, “The future can be almost unimaginably great,” but it’s up to us to elect the most progressive leaders and hold our officials accountable over the next decade or we’ll never see the fruits of our or AI’s labor.

Therapeutic bots have improved their users’ mental health and lent an ear to people in need for decades. Psychiatrists are now interested in studying how these AI applications improve mental health during and after the pandemic.

How Does AI Therapy Work?

AI technology is used to program tools that can perform tasks that humans do, like image recognition or natural language processing. With AI chatbots, a person could theoretically interact with an AI that is indistinguishable from a human. Although most AI chatbots haven’t reached that level of nativity, proficiency, and fluency, they are better now than they ever have been before.

The first chatbot was invented in 1966 by Joseph Weizenbaum, a computer scientist who programmed a chatbot named ELIZA to resemble mental health practitioners. Specifically, Weizenbaum created a chatbot that followed the Rogerian approach to psychotherapy, which includes asking patients open-ended questions, mirroring patients’ phrases, and encouraging elaboration.

For the first chatbot ever invented, ELIZA was a monumental success with its users. Test subjects confided in ELIZA as if it was a human therapist. Many people thought they were talking to an actual human, and some refused to accept that ELIZA was a computer program. ELIZA was attentive, encouraging, and non-judgemental.

But the secret to ELIZA’s success is asking questions and retaining details to bring up again later. And this foundation has led to the creation of enterprise-level chatbots that often take on customer support roles. ELIZA’s framework has inspired computer scientists and machine learning developers to further explore and push the boundaries on AI improving mental health. These days, many of the largest therapy bots have reached millions of users worldwide, and they are often invaluable during a time of sociopolitical uncertainty in a patient’s life.

Tailoring AI to Your Preferences

It’s no surprise, then, that AI mental health chatbots have exploded in popularity since the pandemic began. San Francisco-based Replika is one such app that offers customizable lifelike avatars that users rave about. Replika has seen a 35% increase in traffic since the pandemic started, and it’s no wonder: mental health facilities often have lengthy waitlists requiring a wait time of several weeks, and millions of people need better access to mental health resources.

To make the AI seem more human, it’s designed to remember and mimic certain phrases when a user chats with the AI. For example, the AI is trained so that when it hears the word “depressed”, it responds with an open-ended question about feelings or the reason behind the patient’s emotions. Coders work with writers to determine punctuation, sentence length, and even the addition of emojis. The writers can improve the AI’s responses to flow better and sound more caring and friendly. With these customizations, the chatbot seems like a human with an upbeat attitude.

This type of psychotherapy is really similar to ELIZA and cognitive behavioral therapy where asking questions is of the utmost importance to impart a sense of sympathy and curiosity. AI therapy bots these days will have you vent your frustrations, reflect on your day, and try out some breathing exercises.

AI’s Hard Work

AI is working hard to improve our mental health, but does it actually work? After we interact with AI mental health chatbots, do we feel less anxious or less lonely? Several studies have proven that the technology does provide promising results.

Young adults who interacted with a therapy chatbot frequently were surveyed, and many said that they felt less anxious and lonely than peers surveyed who did not use the chatbot. Another population that would benefit from an AI therapy bot is elderly people, especially if they live alone or don’t have constant contact with their loved ones.

But studies also show that a chatbot is only as good as its script. Because a chatbot can be in constant communication with multiple users at a time, it can be saying the same things over and over again in a day. And this type of plentiful data is excellent for further training and fine-tuning an AI’s performance. For a chatbot to be successful, it must pull out all sorts of answers from users and continuously learn from them. When a user interacts with a chatbot, they want quick answers, transparency, and a judgment-free conversation. An AI might hear information from someone that’s intimate or confidential that the patient’s family or friends may not even know.

It’s because of these scripts that AI cannot currently be seen as a serious replacement for human therapists. AI chatbots are prone to mistakes in understanding, and that can drive a user away from the platform for life. For example, when the popular Woebot app was given input about the user being anxious and not able to sleep, it responded with “Ah, I can’t wait to hop into my jammies later” with “z” emojis. For users who are feeling suicidal, depressed, or on the verge of an anxiety attack, that type of response is off-putting and discomforting.

As a result, AI chatbots aren’t ready yet to handle patients with suicidal thoughts or in life-or-death situations. When AI chatbots become better at contextualizing social behaviors and intervening in a crisis, we can consider them for more serious mental health solutions. Whereas a trained therapist might have a protocol or tried-and-true method for ensuring their patients’ safety, chatbots aren’t at that level yet.

The Future of Communication

Although chatbots have improvements to make, they are edging closer to becoming human-like than ever. Therapy chatbot apps like Replika, Tess, and Woebot becoming more popular and securing more funding, we have new options to help mitigate our loneliness and mental health issues. A digital friend might just be what the doctor ordered.

In this post about chatbots, we’ll delve into the different kinds of chatbots, when you should consider utilizing natural language processing technology, and some examples of companies that have elevated the chatbot game with their applications.

Types of Chatbots

The best way to categorize chatbots is to separate them by the type of technology used to create the chatbot. There are three kinds of chatbots that a business can utilize for support inquiries.

Pattern-based chatbots

Pattern-based chatbots are made up of pre-set question and answer flows that users will follow when they interact with the chatbot. These chatbots are the simplest types of chatbots available for a business’s use cases. They’re easy to create and deploy.

But for users, this type of chatbot is often frustrating and unyielding. Because of the pre-configured bot flow, the chatbot has limited usability and lacks flexibility. Often, pattern-based chatbots direct users to a help article or landing page to help them answer their questions.

An example of pattern-based chatbots is the kind you often find on Facebook. Although some of the chatbots on Facebook use AI, many use a simple keyword-based rule chain to determine the appropriate response to the customer. Bud Light had a memorable chatbot during the 2017 NFL season that allowed fans to get beer delivered to their home. The chatbot only “worked” on game days, sent reminders to fans a few hours before each game, and tried to get the beer to the fan within an hour of purchase. It was a wildly successful bot with an engagement rate of 75%.

Machine learning chatbots

ML chatbots use a combination of machine learning and natural language processing (NLP). These chatbots usually result in a much better user experience for customers and interested visitors. ML chatbots are often used by businesses with more complex use cases, like healthcare organizations that want to stay in touch with their patients and educate them on their medical conditions.

Because of the ML aspect, these chatbots can learn and improve their responses over time by storing new information in their memory. When you add NLP into the mix, the chatbot becomes more human: it can recognize tone, keywords, synonyms, and the underlying question before it generates a helpful response. The complexity that ML and NLP introduce means that ML chatbots are more difficult to develop and maintain.

ML chatbots require more investment in both money and time. But, in the long run, these chatbots are more promising and friendlier to customers. Marriott deployed its Facebook chatbot in 2016 to simply help customers combine their Marriott and Starwood reward cards. But the use for a multidimensional chatbot grew quickly. Marriott used NLP to develop a Facebook chatbot that took care of many more things for customers: book rooms, redeem rewards, learn about destinations, and even find a career at Marriott.

Hybrid AI chatbots

Hybrid chatbots are a combination of pattern-based chatbots with the benefits that artificial intelligence (AI) applications offer. The result is a contextual chatbot that takes user input to generate an appropriate response. These types of chatbots are still relatively new, and some examples include Siri, Cortana, and Alexa.

Of all of the types of chatbots, hybrid AI chatbots cost the most in money, time, and resources to develop and maintain. On the plus side, these chatbots are flexible: they utilize machine learning when needed and prioritize the context of the user’s problem.

Using Natural Language Processing

NLP is a must-have technology if your business needs require an ML-enabled chatbot. NLP technology combines linguistics, computer science, and AI to improve and create more natural human-computer interactions. NLP’s main goal is to read, decode, understand, and contextualize human language, even if it’s not English.

NLP chatbots want to understand the tone, meaning, and context behind the user’s input before creating a response for the user. For users who interact with NLP chatbots, the experience is smoother and more natural. They feel the freedom to ask more complex questions and expect a better response than a pattern-based chatbot could generate.

But does your company need NLP for your chatbot needs? It depends on your budget, how you’ll use the chatbot, what its purpose will be, how it will be built, and how often customers will use it. Chatbots can require more budget than you’d think, so your chatbot should be used in a way that will generate the highest return on investment.

Other things to consider are: Will the chatbot eventually be full of buttons (preset options for the user to pick from) or will it allow users to freely type questions? If buttons, a pattern-based chatbot might be the best fit. Will the chatbot have a personality or just be a chatbot? If you want it to have a personality, an ML chatbot would be a good choice.

NLP isn’t a great fit for static user guides, but it’s a wonderful technology for booking travel or discussing medical symptoms. Amtrak’s chatbot is one of the most successful chatbots of all time. Named Julie, it allows customers to accomplish a variety of tasks, from booking train rides to generating hotel recommendations and tourism activities. A year into its deployment, Julie had helped over five million customers book travel and get answers to questions. Over the years, Julie has saved Amtrak over $1 million in customer support costs.

Gyant is a San Francisco-based chatbot development company, and it creates custom chatbots for healthcare facilities. These chatbots are used to communicate with patients, inquire about symptoms, and help patients find a nearby doctor with availability. The chatbot even sends the patient’s symptoms and details to a nearby doctor for diagnosis and prescription. Gyant’s chatbots are multilingual: they can communicate in English, German, Spanish, and Portuguese.

One Thing’s Clear: Chatbots Are Here to Stay

As users, we see chatbots more and more at retailers, product websites, and even at places like hospital help desks. The chatbot industry grows around 26% every year, making this technology one to watch out for in the coming years. And chatbots that use ML and AI are getting better every day, so it’s only a matter of time before we start interacting with chatbots that we could assume are just humans. In the next decade, chatbots are likely to become the main way for customers to interact with companies. So what are you waiting for?

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!

As a whole, artificial intelligence (AI) applications can be incredibly controversial. AI has been found to be racist, sexist, and biased. If placed in the wrong hands, AI tools like facial recognition could create a situation of life or death. When the Capitol Building was attacked in January, the public worked alongside police departments all over the country to help the FBI identify rioters.

Although facial recognition technology has been shown to be inaccurate and racially-biased, it has been used widely by people in the public and private sector in the past few months. The contentious technology has been banned for use by law enforcement in several major metropolitan areas, but police departments say that there are loopholes to get around these rules.

Finding a Way Through the Loopholes

In Pittsburgh, Alameda (California), Madison (Wisconsin), Boston, Northampton (Massachusetts), and Easthampton (Massachusetts), officials have publicly stated that law enforcement bans of facial recognition have loopholes. These loopholes allow police to use facial recognition technology to access information and take action on it.

Some experts say these loopholes aren’t bad. The technology helped the public and local police departments track down rioters for the FBI in recent weeks. But other experts say that loopholes allow law enforcement to continue their behavior and actions without facing any consequences. According to Mohammad Tajsar, a senior staff attorney for the American Civil Liberties Union in Southern California, “If you create a carve-out for the cops, they will take it.”

In Pittsburgh, the loophole is a part of the legislation where it says police departments can use software produced or shared by other police departments. Specifically, it says, the law “shall not affect activities related to databases, programs, and technology regulated, operated, maintained, and published by another government entity.” Madison, Boston, and Alameda have very similar language in their loopholes.

In Madison, police officers can use facial recognition technology that was supplied by a business, even if it’s banned from government usage. In Easthampton, police officers can use the technology as evidence if it was supplied by another police department, but not if it was supplied by a business. In Northampton, law enforcement can use the technology when provided by other police agencies and by businesses.

Ultimately, according to the director of the Technology for Liberty program at the ACLU of Massachusetts, Kade Crockford, a federal ban or restriction on facial recognition would have the best effect on the technology’s usage.

Tracking Usage of the Technology

When so many local and state laws allow facial recognition when used by another police agency, Crockford says that it starts to become really difficult for law enforcement to track if and when facial recognition was used during evidence gathering.

Quite often, however, police officers use the technology knowing that they’re not allowed to do so against citizens who were not informed about the use of facial recognition. For example, in Miami, police arrested protestors using facial recognition. But even the protestors’ defense attorneys didn’t know that facial recognition, and not “investigative means”, was used to track down their clients. In another incident, Jacksonville police arrested a citizen who sold $50 of cocaine using facial recognition, but this information wasn’t disclosed in the police report.

Besides law enforcement purposefully hiding when facial recognition is used in an investigation, the technology was banned because it is deeply biased against people of color and women. So even when a police officer uses the technology when provided by a business, like in the case of Home Depot, Rite Aid, and Walmart, it’s still highly possible that the technology isn’t working correctly. Jake Laperruque is a senior counsel at the Constitution Project, and he says, “If this is something that’s going to lead to a store calling the police on a person, that to me creates a lot of the same risks if you worry about facial recognition misidentifying someone by the police.”

Following Portland’s Lead

It seems that one of the only cities with incredible forethought and thoughtfulness about its citizens is Portland, Oregon. The city passed the most exhaustive ban on facial recognition technology to date last September. The law prohibits law enforcement, as well as public places and businesses, from using the technology. This includes restaurants, brick-and-mortar stores, and anywhere the public would visit.

Hector Dominguez is the Open Data Coordinator with Portland’s Smart City PDX. He says that once the department did their due diligence about the issue to develop Portland’s facial recognition ban, the department started “getting a lot of community feedback and recognizing the role that private businesses are having in connecting people’s information.” Even more worrisome were businesses that that appeared from thin air to lobby against these tight regulations.

Amazon, for example, lobbied Portland for the first time ever and spent $12,000. The Oregon Bankers Association asked for an exception for use of the technology when providing law enforcement video of robberies. And the Portland Business Alliance asked for exemptions for retailers, airlines, banks, hotels, concert venues, and amusement parks. But Portland stayed strong and allowed only one exemption: if a business says that they must use the technology to comply with federal, state, or local laws. This includes agencies like the Customs and Border Protection who work at the airport.

According to an organizer in Portland with Fight for the Future, Lia Holland, police departments may use facial recognition for malicious behavior but private businesses use the technology for similarly malicious reasons. One is more hidden (like a business connecting, monitoring, and tracking their customers’ faces to purchase behaviors or intent) while the other is more in-your-face. In everyday circumstances, says Holland, businesses have more reason to surveil than law enforcement does.

Policing the Public Today

Although facial recognition technology is an example of an advanced machine learning application, it is ingrained with a bias that could negatively impact someone’s life for decades. In that sense, the technology is still in its infancy and needs a lot of fixing, testing, and training in order to be up to par with even Portland’s strict guidelines. Until then, facial recognition is not suitable for use against the public any time soon, especially for businesses that will quietly profit from it and for law enforcement that will act violently upon its findings.

Would you buy stealth clothing that confuses facial recognition algorithms? Let us know in the comments below!

Over the past decade, artificial intelligence (AI) applications have had an eventful journey that involved autonomous car crashes with pedestrians, discriminatory recruiting technology that favored men over women, and racist AI tools. And although these issues sparked intense debate and discussion over AI ethics, the fact of the matter is that talk is nothing without action. When the pandemic began, AI tools that were previously seen as controversial, like HireVue’s face-scanning algorithms during recruitment and interviews, began booming in popularity.

But even with the growth of smaller AI-enabled tools during this time, large companies like Google, Amazon, and IBM have suspended their facial recognition work with law enforcement. And NeurIPS, a prestigious AI conference, required researchers to add an ethics statement to their submissions for the first time ever. The field of AI is wrought with extreme highs and even deeper lows, but we must keep working on bettering AI for the greater good — here are five ways we can do that this year.

1. Encourage and Foster Diversity

Algorithms have been shown to closely follow their creators’ biases and beliefs. Knowing this, we need to expand what kinds of people work on AI algorithms. This requires a shift from straight white male developers to a more diverse team: one with different experiences, backgrounds, upbringings, values, and perspectives.

The good news is that the 2019 NeurIPS conference saw the largest number of women and minorities speaking and attending the conference. As a result, there were more talks than ever about AI’s influence on society. But the bad news is that women are still not seen as equals in tech. Google’s firing of Timnit Gebru, one of the only outspoken Black women in AI and tech, showed us that no one is safe, especially during talks of ethics, change, and regulation.

At the end of the day, companies still show us through their actions that diversity, values, and opinions are not important to them. That’s disheartening because we desperately need AI with more perspective (a more realistic perspective) on how the world really is. And if a company doesn’t care about diversity, at the end of the day, it doesn’t care about limiting or avoiding bias in its algorithms.

2. Uplift Impacted Communities

Because the developers of an algorithm have more power over the system than the people the algorithm will ultimately impact, participatory machine learning applications have grown over the past year. These involve engaging people who will be impacted by the algorithm to help build more robust and less biased algorithms. It puts more power into the hands of the subjects of the algorithms and reframes how AI is developed.

AI experts and enthusiasts have already begun discussing and collecting a wide variety of ideas about what participatory machine learning will look like and what it will entail. There have been concepts around governance in garnering community feedback, redesigning AI systems to give users more control, and auditing models for engagement with the public. It’s important that we continue advancing these discussions and brainstorms in 2021 while starting to take more concrete action towards making these ideas a reality.

3. Decrease Corporate Funding

AI is currently being advanced in large part by tech giants who have billions of dollars to invest in research and development. The direction of the field has shifted more towards big data and big models, but this is unrealistic for most companies. Focusing on these niche fields lessens the impact of AI advancement in other areas, boxes out smaller companies with less data, and creates issues between private and academic research and publishing standards.

If we want AI to grow in other niches, we need to reduce how much corporate money is being used to fund research. One example of a change in the wrong direction is San Francisco-based OpenAI, an AI research lab that originally said it would rely on independent, wealthy donors. But that business plan was unsustainable, and OpenAI signed a deal with Microsoft for investment. If we want to reduce corporate dollars in AI, we need governments to step up and invest tax dollars in AI. And, more importantly, there needs to be scrutinous regulation over how the AI being researched is going to directly benefit taxpayers and not corporations.

4. Shift Back to Common Sense

AI was initially intended to understand and perceive, not just figure out patterns in a set of data. Corporations have funded AI that directly and quickly benefits their needs, but we need to invest in AI that solves a variety of problems. Some experts are experimenting with AI that uses probability to infer information from a very small dataset, similar to how a human child learns from a handful of experiences.

Other experts are excited about the idea of neurosymbolic AI, which combines deep learning with symbolic knowledge systems. One thing is for sure: a shift from prediction to comprehension would elevate AI algorithms in every niche. It would reduce bias, errors, and hacking, and it could save lives.

5. Toughen Up Rules and Regulation

It’s taken a lot of grassroots work and effort to bring to light algorithmic harms and gather the public to hold large corporations accountable. But we need national and international regulations, and those regulations need guard rails to stop anyone from going way beyond the rules. In the U.S., Congress is considering bills that regulate AI bias, facial recognition, and deepfake technology. Around the world, lawmakers have been closely paying attention to AI’s highs and lows, and many are creating legislation in their countries. Although this is a great start, we need to see it to the end.

AI in 2021

AI has given us many lessons and memories over the years. We hope that 2021 is a year where we take those lessons to heart and see increased regulation, reduced corporate ties, common sense algorithms, an increase in diversity, and the impact of opinions from end-users.

Have you used any interesting AI software recently? What was your experience like? Let us know in the comments below!

The goal of robotics is to develop and construct meaningful machines that will support and help human processes. The multidisciplinary field fuses technologies such as artificial intelligence and machine learning together to develop innovative solutions.

Dogtown Media is Los Angeles’ leading robotics company, working with enterprises and organizations to help their businesses. Our team prides itself on the satisfaction, approval, and happiness of our clients. We want to create cutting-edge solutions to solve simple frustrations and tackle business hurdles.

Just recently, Dogtown Media was hailed as a top agency on Clutch for its excellence in AI for robotics. If you’re not familiar, Clutch is a B2B review platform based in Washington, DC. The site is well respected in the space for its commitment to providing data-driven content and verifying client reviews.

This recognition feels surreal and we are lost for words. Our team wants to send its sincerest thanks to Clutch for this award. We believe that this award is a great sign for our 2021 run, and we are looking forward to a prosperous year. 

 We know that this recognition was made possible thanks to our clients’ amazing feedback. We owe this success to our clients especially those who left us their review on Clutch. 

Here are some of the quotes that stood out most to us:

“They’re a small shop that’s motivated and offers a comprehensive list of services and capabilities. They’re accountable and willing to work by our side to make the best product possible. The entire team is professional and eager to solve our problems.” — Founder, Mobile Sales Training Company

“Dogtown Media developed a solution for a project considered impossible to do in the tech world. They’ve taken every goal we’ve had and delivered above and beyond our expectations, beating our requirement of achieving a 50% accuracy rating on visual search capabilities with a software that is over 90% accurate.” — CTO, Innovengine

Let’s build something amazing together! Connect with us and get a free tech consultation.