Avaamo, a venture-funded enterprise software company, aims to reinvent the way we communicate, transact and work using conversational AI

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They can help you get an appointment or order a pizza, find the best ticket deals and bring your attention to the fact you are spending a lot on entertainment instead of investments. We are talking about AI virtual assistants, which have already become a familiar part of our daily lives. But what technologies are under the hood of AI assistants and how can you leverage them in your business? Find all the answers in this article. Intelligent Virtual Assistants Market Insights Intelligent Virtual Assistants (IVA) also known as Intelligent Personal Assistants (IPA) are AI-powered agents capable of generating personalized responses, pulling from contexts such as customer metadata, prior conversations, knowledge bases, geolocation, and other modular databases and plug-ins. The Intelligent Virtual Assistant market, experiencing rapid growth in the 2020s, is forecasted to reach USD 6.27 billion by 2026, according to Mordor Intelligence. AI assistant technology is in many ways similar to a traditional chatbot but integrates next-generation analytics, machine learning, AR/VR and data science. While conventional chatbots can generate responses to inquiries based on Markov chains and other similar processes, their static responses pale in comparison to the dynamic insights generated by intelligent virtual assistants. One of the best-known virtual assistants is Apple’s Siri, a consumer-facing product packaged as a personal assistant. Examples of other IVAs include Amazon’s Alexa, Microsoft’s Cortana, and Google’s Google Assistant. Siri and competitors help customers easily execute commands with voice prompt, automating tasks such as setting alarms on a smartphone, verbally reading out e-mails with text-to-speech technology, playing and searching for music, and sending text messages. The ubiquity and popularity of IVAs in consumer smartphones led to the inclusion of Intelligent personal assistant technology by car manufacturers. The Asia Pacific region is a critical market to watch when it comes to intelligent virtual assistants, with major growth across the healthcare, technology, and financial sectors. The industry’s heavy hitters include Apple Inc., Inbenta Technologies, IBM Corporation, Avaamo Inc., and Sonos Inc. The end-users utilizing AI assistant technology can be found in the healthcare, telecommunications, travel and hospitality, retail, and BFSI sectors. Consumer products utilizing IVAs or IPAs include smart speakers, smartphones, cars, commercial vehicles, home computers, home automation appliances, and many more. Underlying technologies upon which IVAs and IPAs depend include Machine Learning, Cognitive Computing, Text-to-speech, Speech Recognition, Computer Vision, and AR. We will talk about them in more detail later. Why Do Companies Create AI Assistants? If you’re an Apple device owner, you probably can’t imagine your life without Siri. Amazon Alexa, Google Assistant, Samsung Bixby – the majority of big brands are investing in the development of AI assistants. So why do companies do this? The main advantage of using artificial intelligence to create such solutions is that AI can efficiently and quickly process huge amounts of data, find insights and provide smart recommendations. Powered by voice and speech recognition, AI assistants make it much easier to perform many daily tasks such as adding events to your calendar, setting a reminder, or tracking monthly expenses. According to , there will be over 8 billion digital voice assistants in use worldwide by 2024, roughly equal to the world’s population. Statista The key benefits of building virtual assistants for business include the following: while cutting down on the number of calls and service requests to human agents. With AI assistants you can automate the business flow of interacting with customers. This will allow your employees to focus on more complex tasks and not waste time on requests that can be processed in an automated way. Improved customer support . Customer experience data collected by traditional support calls or chats requires analysts to scrub through countless hours of phone calls and information collected and recorded by a live customer support agent. With IVAs, a customer’s queries and the associated metadata can be instantly filed away and categorized for analysis without the need for a customer support agent to take perfect notes. The ease of key data collection . AI assistants adapt to the needs of each user, providing the client with a high level of personalization. For example, IPAs can remember not only the user’s name but also their preferences. This helps to increase user engagement, as well as improve customer satisfaction and loyalty. Personalized user experience The ability for companies to piece together customer support and complex parts of their corporate toolchain like Lego bricks is one of the biggest appeals of intelligent virtual assistants. With some modification, a virtual assistant can plug into any database, or any resource to provide critical information and optimize workflow at every level. Types of AI Virtual Assistants There are several different types of AI virtual assistants: сhatbots, voice assistants, AI avatars, and domain-specific virtual assistants. have been a mainstay of the E-commerce sector since their inception, but modern implementations of chatbots are powered by artificial intelligence, which gives them the ability to think through customer queries rather than push the customer through a chain of static events. Chatbots use automatic speech recognition and natural language processing to give vocal responses to queries, such as the well-known Siri and Google Assistant products. Voice assistants are 3D models designed to look like humans, used for entertainment applications, or to give a human touch to virtual customer support interactions. Cutting-edge technology from companies like Nvidia is capable of producing nearly true-to-life human avatars in real-time. AI avatars are highly specialized implementations of AI virtual assistants designed for very specific industries, optimized for high performance in travel, finance, engineering, cybersecurity, and other demanding sectors. Domain-specific virtual assistants Also, we can find virtual assistant technologies created for specific tasks. For example, “ based on artificial intelligence and 3D modeling technology allows people with disabilities to perform tasks such as “virtual face reconstruction” and “voice generation simulation” to communicate online freely. Avatar to Person” (ATP) technology The Technology Behind AI Assistants Let’s say you want to create your own virtual assistant like Siri. How would you go about making it? Your first and possibly least difficult option would be to integrate Siri into your application directly. Siri, Cortana, and Google Assistant are three well-known examples of AI assistants that many developers integrate into their applications. In 2016, Apple Inc. announced SiriSDK, a development kit that allowed programmers to integrate functions of their own apps as “Tasks” that Siri could perform. SiriSDK uses “Intents” as labels for user intentions and associates Intents with custom classes and properties. If your company doesn’t want to rely on existing AI assistant options, you’d need an expert team of AI engineers to build your own solution. Let’s dive into the key AI technologies behind intelligent virtual assistants. SPEECH-TO-TEXT (STT) AND TEXT-TO-SPEECH (TTS) If we’re talking about intelligent virtual assistants, they at the very least require Speech-to-text (STT) and Text-to-speech (TTS) capabilities. allows apps to convert human speech into digital signals. This is how it works. When you speak, you create a series of vibrations. Using an analog-to-digital converter (ACD) the software converts them into digital signals and extracts sounds, then segments them and matches them to existing phonemes. Phonemes are the smallest unit of a language capable of distinguishing the sound shells of different words. Based on complex mathematical models, the system compares these phonemes with individual words and phrases and creates a text version of what you said. Speech-to-text does the opposite. This technology translates text into voice output. TTS is a computer simulation of human speech from text using machine learning. The system must go through three steps to convert text to voice. First, the system needs to convert text to words, then perform phonetic transcription and then convert transcription to speech. Text-to-speech Speech-to-text (STT) and Text-to-speech (TTS) are used in virtual assistant technology to ensure smooth and efficient communication between users and applications. To turn a basic voice assistant with static commands into a proper AI assistant, you also need to give the program the ability to interpret user requests with intelligent tagging and heuristics. COMPUTER VISION (CV) is an AI technology that extracts meaningful information from visual inputs like digital images or videos. CV is an integral part of creating visual virtual assistants. These assistants can respond with creator-generated videos, not just sounds, which greatly enhances the user experience. Computer vision Computer vision allows the system to recognize body language which is a significant part of communication. Visual virtual assistants powered by this technology use a camera that stores data and utilizes real-time face detection to catch when someone is looking at the screen, this sends a signal to the rest of the system, which converts the user’s speech into text. CV can also greatly increase the accuracy of speech recognition by comparing what the user has said verbally to the movement of the user’s face and mouth. NOISE CONTROL Noise control is another critical feature for voice assistant accuracy. While many smartphones include software-based noise control and suppression features, you can’t count on this being the case for all of your customers. To compensate for a lack of onboard noise suppression software, top-shelf Bluetooth headsets also include hardware noise suppression, but once again there are no guarantees that your AI assistant is going to be able to detect what your customers are saying in a busy train car. By integrating in-house noise control packages, you minimize the risk of misunderstanding voice queries. SPEECH COMPRESSION Your AI assistant will also need to at least temporarily store voice information for processing unless you’re going to fill up the customer’s hard drive locally with voice data. Speech compression is critical, but developers toe a fine line with compression. It’s possible to compress an audio file so much that substantial amounts of fidelity are lost, making it difficult or impossible to recover what was said during the processing. Compression technology is rapidly improving, but when developing your voice assistant, audio codecs and compression solutions merit a thorough investigation. NATURAL LANGUAGE PROCESSING (NLP) Once you have the voice data, the AI assistant needs to process and interpret the data with (NLP) and then execute the requested command. NLP simplifies the speech recognition process. While many AI kits are pre-trained on countless hours of voice samples, you’d still need enough data from customers to adjust for precision for your use cases. If your AI assistant is going to respond verbally, you’ll need speech synthesis such as Google Cloud’s top-of-the-line solution, which produces realistic and clear voices. Natural Language Processing However, speech processing is not enough to derive a person’s actual intent and maintain a normal conversation. The request still needs to be interpreted right, and that’s when Natural Language Understanding comes into play. NATURAL LANGUAGE UNDERSTANDING (NLU) (NLU) is a different approach to Natural Language Processing and is considered by most computer and data scientists to be a subtopic of NLP. While NLP methods parse, tokenize, and standardize natural language into a standardized structure for command processing, NLU interprets the natural language without standardizing it and derives meaning from queries by identifying the context. In short terms, NLP processes grammar, structure, and compensates for the user’s spelling errors while NLU examines the actual intent behind the query. Natural Language Understanding NATURAL LANGUAGE GENERATION (NLG) produces natural language output. Thanks to this technology, users receive a human-like response from virtual assistants and chatbots. Models and techniques used for NLG can be different and depend on the goals of the project and development approaches. One of the simplest approaches is a template system that can be used for texts that have a predefined structure and require only a small amount of data to be filled in. This approach allows such gaps to be automatically filled in with data retrieved from a row in a spreadsheet, a record in a database table, and so on. Natural language generation Another approach is dynamic NLG which does not require the developer to write code for each edge case and enables the system to react on its own. This is a more advanced type of natural language generation that relies on machine learning algorithms. DEEP LEARNING Chatbots that utilize text-based responses only are substantially less complicated than voice assistants. Because you don’t have to then convert speech into text for interpretation, you remove a lot of tooling from the equation when constructing a chatbot. Next-gen text generation such as GPT-3 is capable of producing not only responses to basic queries, but entire news stories from a “seed”. makes it happen. Deep learning Virtual assistants and chatbots powered by deep learning algorithms learn from their data and human-to-human dialogue. Chatbots that utilize deep learning examine existing interactions between customers and support staff and create paired messages and responses and compensate for the user’s typos and grammatical errors. AUGMENTED REALITY (AR) allows you to overlay 3D objects in the real world for an immersive experience. AR-based mobile chatbots and AR avatars are great examples of using this technology. For example, Arcade created a mobile AR Avatar Chatbot called Miss Perkins for the Ragged School Museum of East London. This assistant serves as a guide for museum visitors and quizzes them ensuring an interactive user experience. Augmented reality Another example of an intelligent AR chatbot was developed for the Vienna Museum of Technology. The creators also used . The functionality of the chatbot includes conducting tours and answering user questions about specific display items in the text, images, videos, and audio formats. mobile AR The rise of the Metaverse and VR technology leads to the logical conclusion of virtual assistants: 3D AI avatars. Combined with artificial intelligence, AR virtual assistants become more functional, bypassing the limitations of existing AR tools. For example, deep learning allows IVAs to capture user behavior in real-time to drive neural networks that automatically train and improve virtual assistant performance. GENERATIVE ADVERSARIAL NETWORKS (GANS) Being algorithmic architectures that use neural networks, create new instances of synthetic data. GANs consist of real image samples and generators fed into discriminators to generate a realistic 3D face for AI avatars and 3D assistants. Generative Adversarial Networks The technology has been utilized in many video games and other products to create true-to-life human figures. GANs can also be utilized to turn still images into full-depth 3D images. Perhaps the most advanced integration of AI avatars so far is Nvidia’s Omniverse Avatar Project Maxine, which creates a photorealistic real-time animation of a human face speaking a text-to-speech sample. EMOTIONAL INTELLIGENCE (EI) When it comes to AI avatars or 3D virtual assistants, it’s not so much the voice that matters, but the body language and human emotions. powered with AI helps IPAs track the user’s non-verbal behavior in real-time when communicating and react accordingly. This will make virtual assistants more responsive thanks to Emotion AI that monitors human emotions by tracking facial expressions, body language, or speech. Emotional Intelligence At the heart of Emotion AI are computer vision and . Facial recognition technology analyzes facial expressions using a standard webcam or smartphone camera. Computer vision algorithms identify the main points of a person’s face and track their movement to interpret emotions. Next, the system determines the person’s feelings based on a combination of facial expressions by comparing the collected data with a library of template images. Solutions such as Affectiva or Kairos can measure the following emotional metrics: joy, sadness, anger, contempt, disgust, fear, and surprise. machine learning algorithms We should also mention recognizing emotion from speech. Such software analyzes not only what humans say, but also how it was said. To do this, the system extracts paralinguistic features that help to identify changes in tone, volume, tempo in order to interpret them as human emotions. Challenges and the Future of Virtual AI Assistant Technology We cannot get around the issue that the adoption of virtual assistant technology is associated with certain challenges. One major obstacle to the future of AI assistant technology is laws concerning data storage and usage. Unchecked use of customer data as training data for AI implementations could easily be challenged by changing data security laws in countries across the world. Controversial data handling policies by companies like Meta (Formerly Facebook) have stoked fears of corporate overreach and privacy concerns after the events of high-profile whistleblower scandals. Therefore, when developing an AI assistant app, take into account the requirements of privacy and data protection, such as in the EU legislation. Make sure your app is fully compliant. GDPR In parallel with the first challenge, there is a question of security and protection from security branches. Security mechanisms such as end-to-end encryption, two-factor authentication and biometrics are some of the best features to protect AI assistant apps. In addition, an experienced team of AI engineers will help you implement custom security systems powered by machine learning algorithms. Despite all the challenges, the future of AI assistant technology looks bright. Advances in technology are also driving the development of smarter virtual assistants. As the NLP process continues to evolve, virtual assistants will be able to perform more complex tasks. In particular, IVAa will be able to make proactive suggestions based on self-learning algorithms and be even more helpful for users. The development of the metaverses is also closely linked in AI with virtual assistants. Intelligent avatars are the best way to provide a user’s identity in a 3D universe. Artificial intelligence is what will allow us to achieve greater realism of avatars. Based on the study of physical movements, the model learns and can, for example, accurately predict the position of the shoulders and elbows depending on where your headset and controllers are. Written by , AI/ML Team Leader at . Evgeniy Krasnokutsky MobiDev The full article was originally published and is based on MobiDev technology research. here

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By definition, a fungible asset is a type of asset that is interchangeable with other assets of the same type. Currency is a fungible asset. After borrowing a $100 bill, the borrower can return the amount either in a combination of bills of different denominations or in a different $100 bill. The value of the dollar $100 remains the same in both cases. Non-fungible assets are not mutually substitutable in the way fungible assets are. Examples of non-fungible assets in the conventional world of finance can be houses, cars, etc. A non-fungible token in the world of blockchain technology and decentralized finance has been developed similarly. An NFT is the tokenized version of a non-fungible asset. Instead of a fiat currency like USD, AUD, or GBP, these tokens may represent an artwork, real estate, or collectibles. Popular digital games, such as Decentraland and CryptoKitties, frequently leverage these tokens. NFTs are developed on the ERC-721 protocol. ERC 20 represents a class of tokens that are identical. Whereas, ERC-721 represents a class of unique tokens. There are a few features that make a token non-fungible. To start with, the ownership of NFT is unique. It can not even be substituted by another matching NFT. These tokens are not separable. Every NFT has defined ownership and privileges. NFT and DeFi — A Promising Union? NFTs are being promoted as the next big thing in the world of decentralized finance. In the first week of September, NFTs witnessed sales amounting to . In the first week of the next month, that is to say in October 2020, the sales of NFT went up to . US$1 million US$2 million According to experts, it is the DeFi paradigm that has helped NFTs to become liquid. It has freed NFTs from being seen as an expensive proposition. NFTs also help to expand the market of collaterals in DeFi lending. A DeFi lending and borrowing platform require collaterals. These collaterals are generally the crypto-holdings. With the introduction of NFT, one can now put other types of assets as collateral. For example, an artwork or a real-estate property can be tokenized as NFTs and put up as collaterals. The use of NFT goes beyond the realm of collaterals. It has the capability of representing more complex financial products. These products can be insurances, bonds, or options. In insurance, each contract is converted into NFT. These NFTs can be traded on a secondary market. Another DeFi model that has been adopted in the world of NFTs in the issuance of governance tokens. Many platforms and NFT marketplaces have started issuing and distributing their governance tokens. Let’s have a look at some of the biggest sales of recent times that used NFTs as the medium of exchange. In July 2020, Picasso’s Bull was put up for an NFT art auction sale. The price went as high as $55,000. After a few days, another sale broke this record. A digital artwork, titled “Right Place & Right Time” was sold for US$100,000. Async Art conducted the sale. On October 7, another artwork, “Portraits of a Mind”, surpassed both these records. The renowned auction house Christie’s sold it for $130,000. More Usage of NFT Using NFT as Digital Collectible This started with the game CryptoKitties in 2017. The game mimics the economic model of asset appreciation. Here non-fungible tokens are sold as cryptoKitties. Users can buy these cryptoKitties and eventually trade them. To explain in a bit more detail, participants buy available cryptoKitties and then breed them to make a CryptoKitty which is rarer than the available CryptoKitties. The participant then sells this CryptoKitty in the secondary market. This game garnered huge attention from crypto-enthusiasts. Some CryptoKitties fetched as high a price as $170,000. Personal Information Verification The NFTs also help in digital identity verification. Since these tokens are not interchangeable and can not be mutually substituted by another NFT, they act as an efficient tool for running personal identity checks. An individual’s credentials such as driving license, academic credentials, passports, etc can be fed to an NFT. This works as a digital KYC. Since it negates the scope of paper documents, which often turn out to be fake, fraudulent, or erroneous, these NFTs are much more secure and transparent. Licensing of Software Any software uses a personal key as its license. This key is a long string of characters, alphabets, and numbers. Users often share or hack these keys to use the software wrongfully. This results in a decrease in revenue for the software company. To stop such misuse, NFTs are used these days. The NFT works as the key in this case. One can keep the NFT in his crypto wallet and use it as and when required without having to share any other personal details. Using smart contract algorithms, companies can also specify the period for which the key is valid. The democratization of High-Value Asset Classes Asset classes like real-estate are expensive. Small value investors can not often enter the market because of the kind of money it involves. NFTs solve this problem by facilitating part-ownership of assets. Recently, a property worth 30 million US dollars was converted into NFTs and was put up for sale. The buyers of each NFT acquired ownership of a part of this asset as per their capability. For the real-estate segment, the deal is profitable because it brings liquidity to an asset-segment that is often constricted by illiquidity. Use in Supply Chain Management The use of blockchain technology to make a supply chain more efficient is not new. With its unique features, NFTs add more transparency to this process. Manufacturing companies use NFTs to help the NFT holders to track all the unique information about a process. The holder of the NFT gets to know the temperature of the meat in each section of the meat processing unit, the quality of the leather at a handbag manufacturing line, the quality of the paint used to paint the bag by holding an NFT. The Future of NFT/DeFi The total market of NFT reached by the end of July 2020. With the DeFi market currently standing at a size of , the NFT space is also expected to grow in the days to come. $100 million $4 billion Some experts believe that as high as new crypto-users will use NFT as an entry point. The important drivers that will fuel this growth are as follows: 4 in 10 The Fun Angle NFTs are used to collect art, game rewards, and similar attractive items. The typical audience of NFT, therefore, does not need to have any specialized knowledge in Finance. The process of collecting NFT is interactive and fun. Therefore, a large chunk of people from outside of the world of finance is expected to get involved in NFT. The Scarcity of Supply The type of assets that are tokenized in an NFT does not go by the standard rule of supply and demand. Assets like painting and artwork increase in value because of their scarcity. For other tradable goods, an increase in demand is met with an increase in supply. But, an increase in demand for Picasso’s artwork will never lead to an increase in supply. This scarcity helps the underlying assets of NFT to grow in value. Attractive to the Asian Market Those who are aware of the crypto industry believe that for any crypto project to be successful, it needs to be successful in the Asian market. With its funny collectibles and gamified interfaces, NFTs have captured the cultural imagination in Asian countries like Japan and South Korea. To conclude, NFT is going to grow in the future because it’s far more inclusive and exciting than the traditional realm of DeFi investments. In the DeFi market, the interest is towards only the financial return. Here, in NFT, the user expects a far deeper reward at the psychological level. With the world becoming full of digital-native objects, NFT is also expected to permanently settle the question of ownership. Also published at https://medium.com/@zild/what-are-nfts-and-what-role-do-they-play-in-defi-2421b132dc43 All opinions in this article belong to the author and do not reflect the opinions of Hacker Noon as a company. Disclaimer:

What are NFTs? - Their Role in DeFi

Cloud Computing has gone from a technology infrastructure trend in the early 2000's to a technology mainstream in 2020. The main difference between "The Cloud" and the traditional computing, which is based on the use of bare-metal dedicated serves is that, on the Cloud all computing services - routing, switching, server instances, all applications - are delivered from virtualized environments that reside on top of clusters of servers. are easy to scale up when it comes to CPU, RAM and Storage. Compute Clouds There are many advantages that make preferred form of IT infrastructure, compared to the physical (also called Bare-Metal) Dedicated Servers. However, this publication isn’t about the benefits of the Cloud. It is about what makes the physical servers a preferred choice for many businesses. Cloud Servers Control Means Control Companies have full control over the IT environment when they colocated their own or use a leased . The physical servers can be migrated fast from one infrastructure points to another (especially those used for private business workloads), can handle significant loads and usually feature increased availability. Dedicated Server Each hardware appliance comes with certain hardware specifications and IT departments have the flexibility to customize their bare-bone IT infrastructure. They can change configurations, add capacity and plan the resource usage. Businesses usually need some form of computing resource redundancy when they buy or rent bare-metal servers and it usually comes either in form of a one time hardware purchase or as a low monthly fee. Unlike the physical hardware, the Cloud means an increased operation costs. Metal Means Metal If any business needs low latency interconnect, a 10 GigE or even higher connectivity, it can plan and get a bare-metal servers with multiple 10 GE interfaces, could use SFP+ or QSFP transceivers. It is easy and inexpensive to connect a physical dedicated server to 10 Gbps, 20 Gbps or 40 Gbps connection.  it just requires a physical server with a certain hardware specifications to be bought or rented. A Cloud Server on 10 Gbps, 20 Gbps or 40 Gbps connection is an expensive service, which requires subscription. High performance business applications, media streaming and many real-time IT analytics services work much better on stand-alone Bare-Metal servers or or interconnected physical servers. For those kind of technology operations, the “good old physical server” is a much more stable and reliable form of an  IT infrastructure solution compared to the Cloud Servers. Dedicated Means Dedicated Any bare-metal Dedicated Server provides guaranteed, physically isolated computing resources in single-tenant IT environment, which can be switched to multi-pennant, through the use of some cluster technologies like Red Hat Cluster Suite, for example. When we run IT service in OS environments created directly on the physical servers, this saves some of the resources which are usually lost on the virtualized environments. The bare-metal servers do not incur the overhead of Virtual Server platforms. Finally, in an event of a natural disaster such as fire, flood or major failure, the use of physical dedicated servers makes it easier for the system administrators to recreate service environments and data from scratch on a new  physical host systems. The IT environments are recovered on a physical hardware where without file formats, partitions or operating systems. Fast growing and big businesses which operate high bandwidth IT environments  traffic and have large resource requirements benefit a lot from a bare-metal dedicated servers. Unlike the Cloud Servers, the physical servers are also preferred option for many regulated industries, for the educational institutions or for the public sector, both to be used in data centers or at the branch as on premise IT infrastructure. So, despite that the Cloud Computing systems are getting more and more popular, many organizations still see a number of advantages in using the “good old” physical dedicated servers and this will continue for at least another decade.

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