Task prioritization is a common issue faced by employees in the workplace. With an ever-increasing number of tasks to complete, it can often become difficult for staff to identify and prioritize which tasks should be completed first. This issue can lead to missed deadlines, low-quality work, and stress among employees as they struggle to manage their workloads. By implementing machine learning applications, however, employees can use data to analyze their current tasks and identify which ones need to be done in order to reach their goals most efficiently.

Some of the biggest companies, such as Home Depot, are rolling out mobile apps that help employees prioritize their work. These apps use sophisticated algorithms to analyze employee workloads, set priorities for tasks, and track completion rates. The apps can comfortably integrate into existing systems and offer a variety of features to automate certain task management processes, streamline workflows and increase efficiency in the workplace. Dogtown Media develops machine learning apps that can help companies improve their organizational efficiencies. 

Advantages of Using Machine Learning Apps for Task Priority Management

Managers of complex tasks can benefit greatly from using machine learning apps for task priority management. As a demand-driven approach to organizing tasks, machine learning apps can provide several advantages that make managing even the most complex projects much less overwhelming. These advantages include automated task prioritization, improved accuracy in task completion rates and progress tracking, reduced manual workloads on team members, better information availability, and shortened completion times. Dogtown Media is well-versed in UI/UX design that can help organizations get the most out of their apps. 

Automated task prioritization is essential when dealing with multiple deadlines and a large number of ongoing tasks. With this feature, managers are able to assign different priorities to different tasks quickly and accurately with minimal effort. This capability makes certain that all of the most important tasks are completed first while streamlining their workflow.

Increased efficiency in task completion is one of the greatest advantages offered by these apps. Aside from simplifying task assignment processes, these applications are also capable of predicting outcomes based on available data, which allows individuals to make more informed decisions concerning which activities should take priority during times of high stress. Additionally, accurate tracking of progress and completion rates provides more reliable forecasting techniques for managerial staff who depend heavily on timely performance measurements for budgeting and staffing decisions.

Furthermore, since machine learning apps take care of the majority of the tedious work associated with task organization and scheduling, team members’ workloads can be reduced significantly. This capability allows them to focus on more productive areas, such as research or outreach activities, instead of having to manually organize assignments or monitor completion rates themselves. Finally, this technology affords users access to near real-time information regarding any changes in metrics or projections. 

This further optimizes decision-making capabilities within a specialized environment by providing pertinent data points whenever needed through user-friendly reports and analytics components embedded into the app itself. Streamlined informational availability goes hand in hand with shortening project lengths. It not only gives individuals quick visibility into current performance levels but also grants marketers the visibility they need before it’s too late. However, there are some challenges involved with developing and using ML apps. 

Challenges with using Machine Learning Apps for Task Priority Management

The first challenge that arises with ML-based task management is the sheer complexity of configuring automated systems to interpret and prioritize tasks correctly. This can be difficult due to the number of factors that need to be taken into account in order to configure the system correctly. It’s important that developers ensure accuracy when configuring automation, as incorrect settings may lead to erroneous output or prioritization issues that could negatively impact business operations. 

To avoid configuration issues and help ensure accurate results, developers should take advantage of ML debugging tools and testing frameworks that can help identify potential problems before they arise and save valuable time in both development and deployment cycles. 

User acceptance issues may arise from employees who are concerned about the automation of task management processes. Organizations must ensure that their machine learning app for task prioritization is set up properly in order to get the most out of it. Proper implementation includes verification of data accuracy, designing and testing the model effectively, and setting up appropriate human input for the algorithms that generate the priority ranking. Effective communication between all stakeholders is necessary in order to ensure the successful adoption of such applications.

Another challenge related to using ML for task priority management is ensuring data security. With automated systems taking over many aspects of task organization, it’s essential that organizations pay close attention to their data security protocols in order to protect sensitive user information from unauthorized access or malicious intrusions. 

Additionally, organizations must be aware of legal implications surrounding data protection, such as meeting GDPR compliance standards or any other applicable regulations in their specific jurisdiction. Organizations can address these requirements by employing methods such as encryption and tokenization for an extra layer of protection during transmission, as well as end-to-end solutions within their networking infrastructure for comprehensive protection over extended periods of time. 

Finally, deploying a successful Machine Learning application requires careful consideration when it comes to resource allocation. While effective utilization of resources is always important when managing tasks manually, this becomes even more critical with ML apps due to the complexity associated with training algorithms or updating existing models according to the latest findings or data changes while still achieving desired performance outcomes over time. 

In such cases, allocating resources efficiently without exceeding budget constraints requires experience gained from working on previous projects as well as understanding limitations imposed by hardware architectures along with computational capabilities available within a target environment before setting available budgets accordingly based on expected returns on investment (ROI). To ensure the successful implementation of such applications, organizations need to take into consideration these potential issues and plan their strategy accordingly.

Working with an App Developer

Organizations looking to create a machine learning application for task prioritization should seek out the services of a reputable app developer. A qualified app developer will be able to guide the organization through the process of creating and implementing an effective ML-based priority management system. The developer will also be able to adapt the system as needed to fit the organization’s specific requirements, such as data types, privacy settings, and workflows.

When selecting an app developer, organizations should invest in one that is experienced and knowledgeable about machine learning applications. They should ensure that the app developer has a proven track record of successful implementations with other organizations. Additionally, it is important for organizations to choose an app developer who understands their vision and can work closely with them to develop a custom solution that meets their needs and expectations.

Attention Deficit Hyperactivity Disorder, or ADHD, is a neurological disorder that manifests as problems with focus, hyperactivity, and impulsivity. It is one of the most common childhood disorders, and can often persist into adulthood. While the exact causes of ADHD are not fully understood, it is thought to be related to abnormalities in brain development and neurotransmitter function. 

The most common symptoms of ADHD include difficulty paying attention, difficulty controlling impulsive behaviors, and excessive levels of activity. Individuals with ADHD may also have difficulty completing tasks, following instructions, and staying organized. While there is no cure for ADHD, it can be effectively managed with medication, psychotherapy, and behavioral intervention. With proper treatment, individuals with ADHD can lead healthy and productive lives.

One promising area of research involves the use of mobile phone apps. Several medical organizations have developed apps that can be used to screen for ADHD and track symptoms over time. In addition, there are a number of apps available that offer information and support for parents and caregivers. As more research is conducted, it is likely that mobile phone apps will play an increasingly important role in the diagnosis and treatment of ADHD. Dogtown Media works with a number of healthcare organizations to develop iPhone apps for a variety of purposes. 

Difficulties Diagnosing ADHD

While ADHD can be a source of significant problems in school and work, it can be difficult to diagnose. This is because the symptoms of ADHD can be similar to those of other conditions, such as anxiety or depression. Furthermore, there is no single test that can definitively diagnose ADHD. Instead, diagnosis is typically based on a combination of self-report surveys, clinical interviews, and observable behavior. As a result, making an accurate diagnosis of ADHD can be a challenging and time-consuming process.

Delays in diagnosing ADHD can lead to delays in the appropriate treatment. The sooner a child is diagnosed, the sooner they can begin to receive the proper treatment. Untreated ADHD can lead to problems in school, at home, and in relationships. It can also lead to substance abuse and other risky behaviors. Early diagnosis and treatment of ADHD can help children and teens reach their full potential. 

Using machine learning to diagnose ADHD

Machine learning is a powerful tool that can be used to diagnose various medical conditions. In recent years, machine learning algorithms have been developed that can accurately diagnose ADHD. These algorithms analyze a variety of data points, including symptoms, behavior, and brain activity. The data is then used to train a machine learning model that can identify patterns that are associated with ADHD. 

This approach has several advantages over traditional methods of diagnosis, which are often subjective and reliant on the personal biases of the clinician. Machine learning provides a more objective and unbiased approach to diagnosis. In addition, machine learning models can be constantly updated as new data becomes available, ensuring that the diagnosis is always up-to-date.

Phone Apps for ADHD Diagnosis and Treatment

There’s no question that phone apps have revolutionized the way we live and work. From managing our finances to ordering takeout, there’s an app for almost everything. Dogtown Media helps the healthcare industry develop mHealth applications to improve patient outcomes. 

In recent years, there has been a growing interest in using phone apps to diagnose medical conditions. Several phone apps have been developed that use machine learning to track an individual’s behavior and look for patterns that may indicate ADHD. For example, one app tracks how often a person checks their phone, as excessive phone use is often a symptom of ADHD. Another app uses machine learning to analyze an individual’s eye patterns, looking for common symptoms.

While there are some skeptics who argue that this is nothing more than a fad, the fact is that phone apps can be a valuable tool for the medical community. By providing instant access to a wealth of information, apps can help doctors treat ADHD more quickly and effectively. In addition, apps can provide patients with real-time feedback on their symptoms, helping them to better manage their condition.

There are a number of phone apps that can help the medical community treat ADHD. An app can be designed to help people with ADHD improve their attention span. It does this by providing users with a series of exercises that they can do to train their attention. It can provide users with a number of tools that they can use to track their symptoms, set goals, and stay on track. Additionally an app can help people with ADHD connect with a coach who can help them manage their symptoms and make lifestyle changes. 

These are just a few examples of the many phone apps that are available to help the medical community treat ADHD. Of course, it’s important to remember that phone apps are not a replacement for traditional medical care. But when used in conjunction with other treatment methods, they can be a valuable addition to the toolkit of any doctor or mental health professional.

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.

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!

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.

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.