Dive into the world of AI in digital marketing through our insightful conversation with an AI innovator, Arjun Rai. As the founder of helloWoofy, Rai opens up about his passion for leveraging AI to level the playing field for small businesses, offering an eye-opening perspective on the potential of AI to transform digital marketing strategies.

In this exclusive interview, we explore the inspiration, journey, and unique offerings of HelloWoofy under Rai’s pioneering leadership.

What was your inspiration to build an AI startup?

At the end of the day, small businesses are what I refer to as “underdogs.” It is not their responsibility to be experts in digital marketing.

But small business owners find themselves asking questions like:

• What is the best marketing message?
• Which Emojis should be used?
• Which hashtags should be used?

 

Most entrepreneurs didn’t go to school for this. They may be specialists in their sector of whatever small business they’re working with, but they’re not digital marketing specialists.  However, at the end of the day, it appears that you must understand marketing in the same way that you must understand breathing. This is where AI in digital marketing comes into play with our company, helloWoofy.

How did you make the leap to AI in digital marketing?

We believed that a tool or platform was required to totally level the playing field for small businesses. And by utilizing AI algorithms, we believed digital marketing “underdogs” could compete equally.

With a vision to redefine marketing for small businesses, helloWoofy innovated our system. We filed a patent for our AI in marketing. And embarked on a mission to allow businesses to produce social media material, as well as blog content, automatically. Then, during the pandemic, we realized that small businesses could contact their consumers by using smart speakers, such as Alexa, which everyone has in their living rooms and bedrooms.

So, around a year ago, we started working with Amazon to create the world’s first scheduler. Now, anybody can be a home shopping network or a TV commercial. With this tool, any small business owner could reach consumers without needing to jump through hoops to showcase their products to millions.

How do you compare to your competitors?

Hootsuite is not just one of our partners but also our second-largest competition. Because many of their clients have also requested the option to intelligently schedule material for social media. As a result, they have excellent analytics, and we have excellent streams.

We are in the process of constructing the world’s first and largest Direct to Customer broadcasting network. The most compelling capability is that small companies are able to directly plan and broadcast material. Additionally, you can create a network where one company owner may advertise and reach another business owner’s audience through an Amazon ecosystem.

The secret sauce? There’s a lot of AI involved in determining who is the best person to purchase from you.

So, there’s a lot here and there, but we want to make sure that the business owner doesn’t need a degree or a Ph.D. to create compelling and engaging marketing messages. 

What makes your AI digital marketing product better?

Our trajectory with technology and AI in digital marketing involves setting up a network that enables business owners to pinpoint segments of their audience—individuals whose purchasing habits can be predicted. This allows business owners to understand not only what their customers are willing to buy but also what they’re likely to buy in the future. We’ve submitted a patent for a unique capability that aids small business owners in discerning which phrases resonate most effectively with their audience and which ones fall flat.

In other words, businesses will know which phrases do exceptionally well and which ones do not perform well. Also, Moreover, it guides them on the optimal use of emoticons and hashtags to enhance audience engagement.

Once you log onto the helloWoofy platform, you simply start typing. The system intuitively fills in your sentence by assessing the words and phrases you’re using and suggests emojis that best fit the context.

You’re probably wondering how we know that. We know this because we’ve looked at hundreds of millions of combinations of words and phrases at scale. And this is one of the four algorithms described in the patent.

We take great pride in the fact that a significant majority of our technology—around 90%—has been developed in-house. We worked really hard to craft digital marketing AI that suits the needs of our users. The remaining 10% consists of off-the-shelf solutions.

The core of our technology is either already patented or has patents filed, ensuring we provide a unique, cutting-edge tool for our users.

What tools do you use?

Alexa-enabled devices control the great majority of the environment. The other devices aren’t as well-developed or developer-friendly, and adoption is low.

Last year, the industry increased by double digits. The vast majority of their devices are audio-only. However, the smart display is slowly but steadily making its way into the market. 

Once we figured out the content creation side, we said,

“All right, let’s get you in front of audiences you’d never have had a chance to meet, and if you did, you’d have to pay an arm and a leg to get there.”

In this instance, you have 100 devoted followers who continue to buy from you on a regular basis, similar to the Oprah Winfrey effect for all company owners.

The post Harnessing AI in Digital Marketing with Arjun Rai appeared first on 🧠 AI Dev Lab.

Collen recounts the pioneering days when he defied the norm by setting the first 3D model on the World Wide Web. His experiences echo the exciting transition from traditional processes to automation AI. He also details his engagement in avant-garde research initiatives such as NIMD that have paved the way for his success.

SapientX’s tech startup impresses us with its groundbreaking approach. Aiming at the junction of symbolic reasoning and pattern recognition, their product elegantly pirouettes on the cluttered stage of AI solutions. It not only outperforms several industry titans but also stands as a beacon of practical tech solutions.

With the introduction of avatars and conversational characters, SapientX raises the bar, intertwining technology with daily human interactions. The thrilling leaps and innovations that have sculpted SapientX’s journey are a testament to their forward-thinking AI strategy, showcasing the amazing benefits of AI.

So, let’s venture forth and explore the motivation, significant jumps, and innovative strides that have delineated the exceptional journey of this AI startup, SapientX. Be prepared to be astounded and inspired!

What was your inspiration to build a conversational characters startup?

As a young architect, I came out to San Francisco a long time ago, and I designed high-rise buildings and accidentally started a software company in the mid-90s. My team put the first 3D model on the internet, which brought us into many fascinating places, doing everything from virtual locations to talking conversational characters. Then, in 2003, the intelligence community invited us to participate in a research effort called NIMD (Novel Intelligence from Massive Data). And they had assembled 18 of the top AI companies in the world to look for bad guys on the internet.

How did you make the leap to get started?

Building and developing our first conversational AI system helped us get introduced to all the top minds at the time. And it was fascinating because all of them were working on different branches of the tree. I think my favorite person through all that was Stewart Card at Xerox PARC. And his partner, Peter Pirolli, both were doing fantastic work, which inspired us.

Another thing that we were working on was soldier tracking systems for the army. We built the first system that could track soldiers in the field inside and outside. And we productized that into navigation systems for the carmakers and the handheld device community. Then, in 2008, my old pal, Bruce Wilcox, became available. Bruce had headed up AI at a number of the big game companies. We brought him onto the team and built what possibly was the first voice assistant for the navigation platform.

What makes your product better?

In the early days of IVR, Bruce thought that was a kind of horse and a buggy thing. And that the future of these voice interfaces was based on conversational ability so that no one would ever have to learn commands. So you could tell your technology what you wanted, and it would understand.

Fast-forwarding to today, we outperformed all the big boys in understanding what people wanted. It’s because we’re taking a different approach. You think machine learning is the be-all and end-all if you’re a young engineer. But the truth is, if you crack open an AI textbook, there are many different branches in the tree. And some of the approaches are better at one thing than another.

So there are three big buckets. First, you have the early folks who are doing what I just generically referred to as chatbots, which are keyword-based systems. Even though they came out around 1965, they haven’t evolved very much. A few years ago, there were hundreds of startups, all doing these chatbots. They didn’t perform very well when interacting with people. So when COVID hit, it was nuclear winter for the chatbot companies because they weren’t doing a very good job.

The second bucket was the machine learning people like Amazon, Google, Microsoft, and Apple. And there’s a reason why they’re almost all big company names. It’s because the systems are super expensive to build and deploy, partly because of their need for insane amounts of data. And the question always comes up, How can you outperform Google?

Does Google have the best engineers on the planet? Why aren’t they doing something better than what we’re doing? And the answer is, yes, they have great engineers. But they’re on a different mission. The only reason Google Assistant exists, or Alexa exists, is to harvest user data. That’s the only reason they exist. And that’s a very different sort of thing that they have built. And they don’t have much motivation to make it work any better. They don’t have any reason to make it work offline because they can’t capture that user data. So it’s different things like GPT3, that’s cool. But you have seen how expensive the computer is that they need to train that model. Some of them run their models on $26 million computers.

So, for us, the proper practical solution is Bruce’s symbolic reasoning method. It’s functional while yet being very light. When running on your Android phone, we only use 2% of the CPU. None of the other systems are capable of doing so. We have been profiling the different methods for many years now. We have seen reports from people in the Echo and Alexa teams over at Amazon. They mention that the system, based on internal tests, scores worse year by year. It started as a rule-based system, and they’ve been adding machine learning to it. And its performance has gotten worse. In their most recent build, there’s now a lot more latency. It took a lot longer for it to respond to me in the morning when I asked him to play the news.

What approach do you follow at this moment?

We were inspired by other research going on, in symbolic reasoning, in particular, research at Carnegie Mellon a dozen years ago. So the way I like to explain it is to say that it’s a little bit like when you were in seventh grade, and your English teacher was teaching you how to break apart a sentence into nouns, verbs, and adjectives in order to understand the meaning of each of those categories. We do the same thing with POS tagging, part of speech tagging.

Initially, we decompose the inbound text into constituent parts. We also performed a little bit of correction. The best speech recognizers right now typically get 96 to 97% accuracy. We improved things a little bit by modifying grammar and sentences. So that’s how we’re able to get up to 99%. By the way, all of our systems and all languages are tested to improve their accuracy until they reach 99% intent accuracy before we put them out.

We are also not a slot filling system. We’re capable of a lot more nuance of understanding than the simple slot-based systems, which are the prevalent systems out there right now. So from there, we’re doing symbolic reasoning with pattern recognition concepts to map into things we know about. And that includes learning about the user to improve that user experience.

One of our projects right now is with Yamaha. Who is having us build a voice assistant for senior care that becomes a friend and a companion that performs smart home tasks? The assistant also monitors the seniors’ health and cognitive abilities using both passive and active systems. Based on the conversations and AI perceptions, we’re able to report to a health team that they should look more closely at what’s going on with him.

There’s a lot that we can do beyond the conversation. We’re also able to adjust conversational abilities by what we learn and to change our interaction patterns based on that, even to the point of understanding and adjusting if you’d like a chatty assistant or a more clumsy one.

The post David Collen, Conversational Characters and SapientX appeared first on 🧠 AI Dev Lab.

Here’s a deeper dive into how AI’s influence reshapes the ecommerce landscape.

Elevating Customer Experiences with AI

Central to AI’s impact on ecommerce is its unmatched personalization. Ecomm sites are transforming shopping experiences using specialized types of machine learning.

Firstly, they delve into each customer’s unique preferences, browsing habits, and purchase history. Then, this tailored approach lifts the shopping experience, making every action online feel custom-made.

This level of detail transforms shopping, ensuring each interaction is distinctively personalized.

Personalized Product Recommendations

Wish you could get recommendations that seem handpicked just for you? AI-powered ecommerce sites are doing exactly that. By analyzing your past behaviors and choices, they offer products you’re likely to adore, essentially serving as your digital personal shopper.

Customized Content and Deals

Moreover, ecommerce don’t just personalize products. They also tailor content, promotions, and deals. By leveraging insights from your shopping behavior and preferences, every promotion you encounter is designed to grab your attention, making offers more relevant and shopping more efficient.

Expanding on personalization and tailored deals, ecommerce is continually exploring new frontiers:

1. Dynamic Pricing
   Adjusting prices in real-time based on demand, availability, and customer profiles to offer the best deals.
2. Chatbots
   Offering real-time, 24/7 customer support and shopping assistance that feels warm and personalized. (Learn more about how to create a chatbot for your website.)
3. Augmented Reality (AR) Shopping
   This allows customers to see how products would look in their space or on them, enhancing their confidence in purchase decisions.
4. Predictive Analytics
   Forecasting trends and customer needs ensures that your favorite products are always in stock and that new discoveries await you on every visit.

This intensive personalization, fueled by the growth in AI, is transforming ecommerce into a realm where every interaction is tailored, every recommendation feels handpicked, and every shopping session is a bespoke experience. It’s like bringing the luxury and attentiveness of a high-end boutique right into the digital world. Welcome to the future of shopping, where it makes every visit to an ecommerce site about you, and only you.

Revolutionizing Product Searches

AI is totally transforming the way we search for products online, making it more intuitive and fun! Gone are the days of only typing keywords into a search bar. The tech brings voice and visual search to the forefront, turning the shopping process into a breeze. Let’s dive into how:

• Voice Search
  With devices like Amazon’s Alexa and Echo, say what you’re looking for and get it without lifting a finger. Shopping is as easy as having a conversation.
• Smart Contextual Understanding
  Beyond specific searches, smart technology understands the context of your needs and even anticipates what you might like before you finish typing. This means search results adapt to fit your personal tastes and preferences, offering tailored suggestions based on your past interactions.
• Visual Search
  Spot something you love? Take a photo, and visual search technologies will match it online. eBay’s advanced algorithms ensure you find exactly what you need quickly and accurately.
• Augmented Reality (AR)
  AR lets you visualize products in your space or on you before buying, merging digital views with physical reality for a confident purchase decision.

This evolution in product searches is ushering in a new era of shopping, where finding what you need online is smoother and more personalized than ever. Welcome to the future of shopping, where every search is tailored to your unique journey.

Enhancing Online Security

On the security front, as ecommerce evolves, so does the protection of transactions. Advanced technology is raising the bar for safety, protecting both shoppers and businesses alike. Here’s a closer look at how AI is making its way into ecommerce website development and security.

• Instant Fraud Detection
  Sophisticated algorithms quickly identify and alert on potential fraud, creating a safer transaction space and lowering the risk of breaches.
• Advanced Authentication Methods
  Biometric and behavior-based verification methods offer stronger, personalized account protection against unauthorized access.
• Robust Encryption Techniques
  Improved encryption safeguards personal and financial data online, adding an extra layer of security.
• Predictive Analysis for Fraud Prevention
  Predictive models spot potential threats early, enabling proactive steps to prevent fraud.

It focuses on preventing threats before they become a problem, rather than just reacting. Thus, we’re entering a new era of ecommerce, marked by advanced security integrated into every shopping experience.

Now, when you click “buy,” you can be confident. Welcome to the future of safe online shopping, protected by the latest security innovations.

Streamlining Operations

Once correctly developed and implemented, machine learning ensures operations proceed smoothly. By adopting inventory management systems, businesses gain superior control over their stock. Thus, systems can accurately forecast necessary stock levels, streamlining supply chains and cutting costs. Now, picture the efficiency and savings as overstocking or stockouts vanish!

How AI is making its way into ecommerce website development by streamlining the operations.

• Smart Inventory Management
  Systems that automatically adjust stock levels based on real-time sales and forecasted demand.
• Supply Chain Optimization
  Models analyze logistical data to suggest the most efficient routes and methods, reducing delivery times and costs.

Elevating Customer Service

Step into the future of customer service, where various chatbot types are always ready to assist. These bots provide answers, solve problems, and handle transactions 24/7. Also, doing this without ever needing a break.

The result? Customers receive timely assistance, boosting their satisfaction greatly. Moreover, this technology manages routine inquiries, freeing up your team.

Among the following are customer-facing examples of how AI is making its way into ecommerce website development.

• Chatbots
  Offering instant support, these chatbots can handle a wide range of inquiries, from tracking orders to answering product questions.
• Personalized Support
  Tailor interactions are based on a customer’s history and preferences, providing a more personalized service experience.

By leveraging advanced technology, ecommerce platforms are elevating the customer experience and transforming operational efficiency. Consequently, this dual approach guarantees that businesses deliver top-notch service and operate more efficiently. Welcome to the era of intelligent e-commerce. Here, technology underpins both unparalleled customer support and operational agility.

A Future-Proof Ecommerce Strategy

The integration of artificial intelligence into ecommerce website development marks a pivotal shift towards smarter, more personalized, and secure online shopping experiences. As this technology continues to evolve, its potential to transform every aspect of ecommerce becomes increasingly apparent. For online stores looking to thrive in this new landscape, you must embrace how AI is making its way into ecommerce website development. It’s a necessity for future-proofing their platforms and staying competitive.

The post How AI Is Making Its Way into Ecommerce Website Development appeared first on 🧠 AI Dev Lab.

From his early days at Cisco to pioneering a developer-friendly platform leveraging the latest in deep learning research, Fox’s journey is a strong example of the growth of AI. This narrative not only showcases the amazing benefits of AI.

Let’s delve into his inspiring narrative, showcasing the revolutionary impact of audio intelligence in the tech industry today!

What was your inspiration to build an AI Startup?

I began my career with Cisco, where I joined a Machine Learning team and worked on a lot of NLP projects. We had a project that required speech recognition. But, even at Cisco, we were not going to build it ourselves.  We attempted to obtain API access from several legacy companies, such as Nuance. However, it was unexpectedly painful.

Google had released its first public speech-to-text API at the time. And we wanted to play around with it, and it was actually rather excellent, but there was no support.

I’d become interested in deep learning research and noticed that there was a lot of work being done on speech recognition and how it was improving.

So, it was a combination of factors that inspired me to think, “What if you could build a Twilio-style company using the latest deep learning research that was just much easier for developers to use with a much better developer experience and better tech because it was using the very latest research, a Brand-new speech recognition API?”  And it was from there that the idea grew.

How did you make the leap to get started?

In 2017, I left my position at Cisco, and I was doing freelance work to supplement my income while I was working on getting the company off the ground.

 Even though I didn’t feel ready, I applied to Y-Combinator because I thought it would be a good exercise. Even though it was about a month after the deadline, I got an interview and was accepted. I was honestly really surprised. From one week to the next, it went from zero to sixty. That made my head spin. But that’s how it all began.

What makes your product better?

Our customers can now perform a lot more than merely transcribing. We can give them access to plenty of extremely cool use cases, applications, and features. They get audio intelligence that they can easily create on top of the transcription with just a single API parameter.

As a result, it all starts with those basic use cases. People immediately realized, however, that we now have all of these phone calls transcribed. We can, for example, look at the keywords in those or summarize them. Alternatively, if you’re undertaking sentiment analysis, we can do the same. Many of our customers request us to transcribe information for them, such as their Zoom meetings.

We focus on the developer experience. The legacy firms lose that sense of urgency and anxiety that smaller organizations have. As a result, the legacy companies’ lack of thinking about the need to continually upgrade their technology has truly fallen behind. 

How does your audio intelligence compare to competitors?

Customers select us because we can give more than what you can get from a major tech provider like Google or Amazon. It’s also giving them the assurance that they will receive the finest assistance and that we will have a staff to assist them.

The Problem with the legacy providers is that:

You can’t have it as a fundamental component of your solution, and it’s not like everyone else who develops on AWS or GCP. However, we’re talking about services that aren’t adequately supported.

• You can’t have it as a fundamental component of your solution as a differentiator, and the services aren’t adequately supported.
• They aren’t essential to their business.
• They might be switched off at any time.
• They are only updated once or twice a year.
• You can only have anything special done or affect the plan if you spend millions a year with them.

We spend a lot of money using Amazon Web Services. Even yet, it seems like we get a new account manager every two months. They don’t know who we are or what we’re up to. As a result, there is no connection.

That is why people seem to prefer us over big tech. We make it more personal to you. As a result, it feels more like a collaboration and an extension of your product.

What tools do you use?

We use PyTorch to create all of our models and audio intelligence. So, for voice recognition systems, we’re developing end-to-end deep learning models. Many of the other models we developed, such as entity recognition and topic detection, are all deep learning-based, as are automated punctuation restoration. Convolutional neural networks are still really powerful. Transformers, attention based models, are really powerful.  We train really big models on dedicated hardware with somewhere between 100 and 150 GPUs at this time.I believe that our transcription models are currently the largest. Training on 32 GPUs takes around six weeks.

The post Audio Intelligence with Dylan Fox, Founder of Assembly AI appeared first on 🧠 AI Dev Lab.

Get insights into AI in business strategy, as Tobias explains from Whoelse AI’s contributions to DIN Standards and ISO protocols. Additionally, understand how the growth of AI influenced his innovative venture that aims to simplify online services and standardize voice AI interactions.

What was your inspiration to build voice AI?

I’ve worked in the technology business for public and private organizations for the last ten years, including the European Commission, The German Institute for Standardisation(DIN), and some corporate consulting companies. 

Whoelse AI was created because we discovered that over the previous eight years, customers of all ages had difficulties remembering how online services operate. The idea came to me when I explained how different internet services worked to my nephew and grandmother at the same time.

And it occurred to me at the moment that rather than trying to explain that AirBnB was for apartment sharing, Tinder was for dating, and Ticket Master was for event tickets, it could be easier to explain using Whoelse AI. The aim was to establish a single brand that could describe any type of Internet business, A concept created in answer to the issue of explaining technology from a pedagogical and social standpoint.

It turned out to be a valid question about AI interoperability because numerous groups are now working on a standard to use it on voice AI assistants.

How did you make the leap to get started?

Everyone is familiar with Amazon’s Alexa and Apple’s Siri. However, there are over 1000 voice AI technologies on the market. And it makes you wonder: What type of wake word should these voice assistants respond to?

So, if you say, “Alexa, hello.” You know that is an Amazon device, this is the Amazon standard, and there are more standards on the way, such as the World Wide Web Consortium (W3C). The Internet Protocol Standardization Association, there’s even an initiative called Voice Network. They’re working on a register for Voice Internet services. Using the same analogy, a more straightforward name for voice and a simpler name for Internet services, 

We believe this becomes now relevant because voice AI assistants need a wake word. And the question will be, What kind of wake word can you navigate a voice assistant in the easiest way? Because voice assistants work without any screen, you have to explain it to explore the usability using your own voice.

That’s why we believe that this was the moment for us to build up WhoelseAI. Since the last couple of years, the topic has become more relevant, so we decided to do it now.

Image courtesy of Whoelse AI

What makes your product better?

We launched the project in 2018. We introduced the project first to the DIN Standard (the ISO’s Standard German representation). The ISO Standard regime is unique because it is usually a requirement in public procurement processes, which means that if the European Commission is writing government contracts in Europe, they must use ISO standard before they can use any kind of proprietary standard and because we have been contributing with the ISO Standard with our research. And in this way, we could gradually ensure the efficiency of our own business.

How does Whoelse voice AI compare to competitors?

We’re all working together to write the technical specifications for voice AI collectivity. In addition, we aim to create a human language protocol. The question is whether the protocol for human language can be implemented using the current standards. As a solution, we developed a standardized framework for encoding any language. 

And, in this field of competing standards, we are not seeking to produce yet another standard or the greatest standard currently available. We attempt to provide the most basic standard feasible, and we will not do so for any technological component. We’re only doing it for the sake of linguistic structure. So, for example, we’re collaborating with the World Wide Web Consortium (W3C) on technical parameters for voice AI collectivity, and we’d want to develop a protocol for human language. The question is whether the protocol for human language can be implemented using current standards.

Because every language is slightly different on the inside, we develop an encoding of every language in a standardized manner. And we don’t believe that the German, French, and Italian firms will agree on a common denotation. And that creates a problem for the companies attempting to use AI for their products. To use AI, they have to either use an already existing company, like Amazon, or build a new one, but it can be a lot more expensive.

What tools do you use?

We serve as a bridge between ARM and IBM Watson. Utilizing the DIN Standard that we created, we also offer the protocol used with it. The problem is that sophisticated models must be trained and developed. Because the larger a language model becomes, the less accurate the model is. I have maybe 100 distinct intentions that I can put into this instance of IBM Watson. So, for example, I can use up to 80 extra intents on my hardware platform like Hey, find me a cinema, playing “XYZ” movie, And then IBM Watson can ask the Cinema ticket Whoelse AI. Then we say, okay, cinema home ticket Whoelse AI can be set by provider “XYZ” that just uses any other system. It will work like a phone automated line that you can use to request information or do tasks.

We’re always thinking about voice assistance becoming a globally intelligent assistant who knows everything about my schedule, my bank account, and so on. So, we’re on the same page, a one-size-fits-all approach to artificial intelligence assistants. 

My vision is that there will be various words with varying capabilities. The initial function of just forwarding user requests. In the long term, it will also be an interconnection between these various AIs, but for now, it’s simply the forward request, and the platform will perform it. And we showed it in the DIN Standard, which we created using Google Dialog, Slope, Nuance, or IBM Works.

The post AI Voice with Tobias Martens, Founder of Whoelse AI  appeared first on 🧠 AI Dev Lab.