Inbenta AI

  • Are Chatbots Actually Automating People?
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    Automation is pretty cool. It shields us from mundanity, and gives us back  time and resources we could better spend on more fun or productive things (or perhaps just things that we haven’t figured out how to automate yet). Because of the allure of automation, bots are sometimes perceived as a threat to humans, particularly their employment. Saving time? Saving money? Objectively, any business should strive towards that. Performing business tasks at a fraction of the cost in less time, that’s practically the definition of efficiency. The fallacy that bots will replace humans in the workforce surfaces with this question: are we actually automating people?   We like to make chatbots appear as human as possible — culturally-appropriate names, carefully constructed avatars, colloquial vocabularies, jazzed up dialogs to make users feel like a real conversation is taking place — but bots are not human. Nor are they autonomous. Have you ever seen Blade Runner? Consider it a blessing and a curse. Forget about free will, and consider this: bots lack real intellect — they know what we teach them, which we must spoon feed them in inevitably limited doses. In fact, because of this, many bots in today’s market aren’t that cost-cutting at all. Chatbots that rely solely on machine learning, which is almost all of them, require large amounts of utterances and training datasets to be remotely efficient. The high initial investment doesn’t even pay off, because while you still need to train the bot as time goes on, with the passage of time comes the depletion of utterances that your bot can learn from. Inbenta offers a chatbot that works a little differently, offering 90% accuracy from day 1 with no training or utterances required for launch. Our technology leverages natural language processing for more conversational exchanges with users. This means we make a chatbot that’s better at being an intelligent bot, not one that’s better at being a human being. Humans have a value that is unparalleled by robots. Our ability to act with both logic and empathy is a feat unsurpassed by bots. Our creativity bleeds through our work in ways that are both subtle and undeniable. We are able to relate to each other because we are each other. We bear the joys and brunt of culture, language, relationships, ethics, and responsibility. Our brains can identify and make connections between things that would take bots years to learn, if at all. We learn on our own, we don’t require supervision, extensive training, or debugging. We can be held accountable for our actions, and know not to run off on discriminatory Twitter tirades. These are all qualities we need in the workforce to be able to work with and for each other, accomplish great things, and do it all in the most efficient way possible. We might not be working 24/7, but those 8 hours a day, five days a week that we are make up for it, and then some. Bots are sophisticated tools — we build, use, and optimize them that way. Cars didn’t teach themselves how to drive, just like how chatbots didn’t teach themselves how to… chat. Behind every bot is a team working diligently and constantly to make it useful and convincing, and there’s still a lot of work to be done to make this variety of “automation” happen.   The post Are Chatbots Actually Automating People? appeared first on Inbenta. Read more »
  • Uncanny Valley: The Dark Origins of the Robot
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    In today’s age of modern technology, we’re presented a limited, marketable spectrum of what robots can be: helpful, futuristic, profitable, and sometimes cute. However, the underlying concept of service bots predates the business goals of the Fortune 500, and is deeply rooted in literature and mythology. What does the word “robot” mean? The term “robot” comes from the Czech term “robotnik,” meaning “slave.” The term was coined by twentieth-century science fiction writer and Nobel laureate Karel Čapek, who first demonstrated its use in his short 1920 play R.U.R., which portraying manufactured, android-like workers that emulate humans in form and anatomy, but lack souls. Spoiler alert: as the story goes, the robot slaves rebel against their human masters. Have you ever felt uncertain, or even threatened by a robot, or another persuasive yet inanimate character that appears to be living? You’re not alone. As artificial intelligence evolved throughout the decades, these fictional robots began to adopt the form of material objects — from wooden dolls to machinery. However, the unmistakable yet intangible threat that robots pose to humans has remained largely constant throughout both literature as well as the human psyche. From Uncanny Valley to Silicon Valley E.T.A. Hoffmann, the author behind the work that inspired The Nutcracker, explored our automaton counterparts in gothic short stories and novellas — from characters falling in love with life-like, human-sized wooden dolls, to capturing disgust towards human figures and personalities corrupted by the soulless and wicked. His work pioneered our understanding of the “uncanny valley” phenomenon years before the term for it erupted during the rise of computers and CGI, a term which describes our inherent revulsion to things that appear off-human, creepy, or even a threat to our human distinctiveness. The term was formally canonized in 1970 by robotics professor Masahiro Mori to describe the relationship between how more human-like an object is, the more we’re disturbed by it. You can see the literal valley in this graph: Examples of this phenomenon include common reactions to humanoid robots, CGI face-reconstruction (e.g. Princess Leia in Star Wars: Rogue One, or Jeff Bridges in TRON: Legacy) and mechanical dolls in theme parks (e.g. Disney World’s Hall of Presidents, and most other things at Disney World). You can already see that the most acclaimed commercial robots have been largely distinct from the human form. Robots like Sony’s Aibo, a mechatronic pet dog, and Jibo, a robot that has been described as “socially charming,” have been physically abstracted and exaggerated in order to appeal to a broader audience. Are humans the problem? With deep roots in fear and ethical gray-areas, it’s no wonder that our view of robots today hasn’t changed all that drastically. But is that OK? Humans tend to abuse chatbots, robots, and voice assistants wherever the opportunity to do so exists. Alexa, Siri, food delivery robots — all bear the brunt of our attitude towards subhuman entities, manifested in the form of verbal disrespect, curses, or even physical violence. By and large, robots are expected to diligently serve and attend to our needs while enduring our abuse. Will we ever reach a point where we can create bots that are completely indistinguishable from humans, superseding the uncanny valley? And if we fail, how far will it set us back?   Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company   Let’s get in touch The post Uncanny Valley: The Dark Origins of the Robot appeared first on Inbenta. Read more »
  • 4 Customer Service Tips for the Holiday Season
    Each year during the holidays, online shoppers all but break the internet. In 2017, online shopping sales set a record high of of $108 billion. This poses a challenge for online retailers, not only in handling the mass influx of customer support inquiries, but with support resourcing and online security. With a good support strategy, online retailers can divert most of these challenges to self-service; however, there are a few precautionary measures to take in advance in order to ensure the smoothest experience for your online shoppers (and support agents on the other side). Prepare your site and online support team with the following tips. 1. Review your self-service content and FAQs. This season, you can expect an avalanche of customer support inquiries. Naturally, your customer support team won’t be able to handle 100% of incoming calls, emails, and support tickets. Now is an excellent opportunity to spend time and resources reviewing the content you already have, review past user questions, and forecast what new content you will likely need to get past this year’s shopping rush. 2. Prepare your cloud-based solutions. As more and more solutions are based in the cloud, make sure you have plans for either auto-scaling your infrastructure, or extend your servers, databased and network capabilities to absorb the incoming increase of traffic. 3. Prepare for mobile shopping. Last year, shopping via mobile devices — including tablets — accounted for $35.9 billion of holiday sales. That’s a 28% increase from 2016. As if mobile responsiveness wasn’t crucial enough to shifting consumer behavior paradigms, now is the time to assess your site’s mobile performance to preemptively combat site malfunctions or missing resources that might impact the user’s shopping experience. 4. Reinforce your security. There have been a number of malicious security incidents throughout 2018. If you periodically make changes or new deployments on your website, especially in the past few months, then now is a great time to conduct an extensive test to evaluate your website security. Try completing your security review before the holiday season, which will make this incredibly difficult. Check out more security tips here.   Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company   Let’s get in touch The post 4 Customer Service Tips for the Holiday Season appeared first on Inbenta. Read more »
  • Case Study: Machine Learning vs. Natural Language Processing
    There are 2 kinds of Natural Language Processing… Today, industry-leading NLP is built on AI that detects patterns in data that can then be leveraged in understanding user inputs. This creates an approach that is flexible in adapting to the nuances and ambiguity of languages while boosting accuracy between results and user intents. However, there still exists implementations of an old-school approach to NLP that relies on machine learning algorithms and predetermined rules. In this approach, algorithms are fed what words and phrases to detect in order to return correlating responses — this imprecise method of “understanding” language results in limited accuracy. With the latest strides in technology, there’s no acceptable excuse to continue using basic machine learning and keyword-driven algorithms as a substitute for modern, sophisticated natural language processing. We put our NLP to the test. In a short comparative exercise, we put another NLP-driven chatbot against Inbenta’s own to examine their real responses to “natural” language. The following questions were posed both to the Inbenta Chatbot and to another popular chatbot service on the market that advertises its use of NLP. For the sake of this comparison, we’ll refer to it as the anonyBot. Without making any changes to our lexicon, we took the FAQs used by anonyBot and plugged them into our Inbenta knowledge base. The results of the experiment pointed out a clear discrepancy between the two conceptions of Natural Language Processing. To further demonstrate one of the many steps in our NLP process, we’ve tagged each result shown here with its semantic score — a calculated percentage of how close of a match the ending result is to the user’s original query. How semantic scoring works: Using Inbenta’s NLP stack, we’re able to abstract a query down to clusters of lexical units, functions, and concepts that represent meaning, or intent, rather than language. We then use an algorithm that ranks existing contents to the intent, and the strongest match is returned to the customer in the chatbot’s response. The Experiment: Question 1: is there a limit of the size of db? anonyBot:No answer [✗] Inbenta: How large a knowledge base can I create? [✓] Our semantic score: 58.8% Question 2: which formats do you support? anonyBot: No answer [✗] Inbenta: What format does the tool expect the file content to be? [✓] Our semantic score: 80.9% Question 3: I forgot my password anonyBot: No answer [✗] Inbenta: How do I login? [✓] Our semantic score: 57.1% Question 4: I made i change but I can’t see it yet anonyBot: No answer [✗] Inbenta: The updates I made to my knowledge base are not reflected on publish. Why not? [✓] Our semantic score: 58.4% Question 5: is my data secure? anonyBot: No answer [✗] Inbenta: How safe is my knowledge base data? [✓] Our semantic score: 92.4% To learn more about the sophistication behind real natural language processing, check out this high-level summary delivered by Inbenta Co-founder and CEO, Jordi Torras.   Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company   Let’s get in touch The post Case Study: Machine Learning vs. Natural Language Processing appeared first on Inbenta. Read more »
  • 6 Ways to Securely Implement your AI-based Chatbot
    Inbenta has extensive experience deploying intelligent, conversational chatbots throughout large enterprises. Over the years, we’ve learned a lot about cybersecurity measures, what to prepare for, and what to guard against. After a more recent in-depth review, we’ve outlined the following best practices for securely deployed your AI-based chatbot onto your site. Understanding the risks In order to be truly useful, chatbots must be available in as many places as possible within a customer’s workflow — this allows a chatbot to provide customers with contextual help throughout the process. Most AI providers offer their enterprise technology through Software as a Service, so in many cases, having the entire AI software in your servers is not possible — or even desirable. The inability to contain the chatbot on your protected servers may lead to increased security risks for your company and customer data. Below, you’ll find six tips to maximize your security when deploying AI chatbots and conversational interfaces onto your website. 1. Secure your JavaScript In many cases, chatbots are deployed onto websites using JavaScript snippets that are dynamically loaded into web pages. These snippets create the UI for the conversational agent. Although this is not intrinsically dangerous, there are few recommendations that should be taken into account: Do not include external “< script >” tags in your critical web pages, like the login window, or the check out page. When you do include those external scripts, use a Subresource Integrity with your “< script >” or “< link >” tags to external sources. When possible, include and host all necessary scripts in your secured web server. 2. Secure your access to RESTful API services Choose providers with at least access to a RESTful API with a two-layer authentication. This should include security keys with temporal access tokens, ideally with origin verification, like domain keys. Keep the API security keys safe to limit access to the API services. Lastly, remember that accessing your chatbot AI software through RESTful API will often need more coding resources from your side but, in general, it will provide a more secure environment for mission critical applications. 3. Secure your webhooks Webhooks allow a chatbot to interact with other systems in the backend. An intelligent chatbot will have limited capacity to help users if it cannot access CRM, databases, billing systems, etc. While this is inherently not insecure, some caution is necessary. Secure your webhooks by means of implementing an authentication layer and validating the origin of the requests they receive. Also, only allow encrypted communications through HTTPS. 4. Keep all passwords secret and safe Most development environments for chatbot providers use some sort of web-based Integrated development environment (IDE) usually accessible by username and password, so make sure you keep your password safe. Choose a password that maximizes security, and change your password often — and of course, never, ever share usernames or passwords with co-workers. 5. Maintain your software stack with frequent updates The vulnerabilities of different software systems are often known by a whole community of cyber attackers, so keep your software stack updated with the latest versions in order to keep your applications safe. Hackers are continuously scanning the Internet for vulnerable systems. With a small, simple script, they can scan every IP address to look for some particular known vulnerability. When they recognize a vulnerability in your system, they begin a recurring onslaught to your software stack. 6. Protect the privacy of sensitive data Do not request sensitive information through any chatbot data flow. If strictly necessary, then make sure that the chatbot only handles the minimum information required to validate the identity of users and let the back-office and secure system specifically designed to handle sensitive data carry out the necessary operations. As an example, don’t make your chatbot ask for a credit card number. Instead, ask for a randomized digit, and let a back office system validate the information. Safety doesn’t end here. These guidelines are only one part of maintaining a safe and secure system. For customers, it is just as important to follow the latest security updates and safety guidelines when using our technology — or anyone else’s technology, for that matter. Together, we can work to keep the Internet and its applications a safe, fulfilling space. At Inbenta, we are specialists in Artificial Intelligence for Natural Language Processing, a complex science that involves understanding how humans communicate, and letting computers understand the nuances of complex communications. We also have learned the importance of security when deploying chatbots that tend to have a great impact on the overall user experience. To ensure the constant safety of our customers and their users, we work together with external security companies to keep our infrastructure as safe as possible. The post 6 Ways to Securely Implement your AI-based Chatbot appeared first on Inbenta. Read more »
  • Chatbot UX: 6 Ways to Keep Customers Engaged
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    By now, we’ve learned well that chatbots wield the power to radically bridge businesses and consumers through scalable conversations. Instead of the one-way efforts of traditional marketing, the limited bandwidth and knowledge of a salesperson, and the time-consuming process of standing in for your purchase to be processed by a cashier, chatbots mold all of these steps onto one platform, in one conversation. The difference is just as stark when compared to the norms of digital shopping. A user browses for items that meet their requirements, checks reviews from around the internet, sends inquiries to customer support via email — the experience, though limited to the web, still takes a user on a journey that is largely unconfined. While chatbots have the power to streamline the customer journey, your user’s attention is confined to a chat window, meaning there’s a lot more pressure to succeed. Essentially, you must remember that the chatbot experience is, ultimately, a redefinition of the shopping experience. Beyond designing buttons and color palettes, UX encompasses the parameters of user flow and conversion. It is critical to employ thoughtful user experience design to ensure that your customer is getting the most out of your bot, and that your bot is producing the results you want for your business. Let’s explore a few key elements to chatbot UX. 1. Avatar Frequently, before a user has the chance to initiate your chatbot, they’ve likely already seen what it “looks” like: the sentience in front of the JavaScript. People on average respond better to human faces; however, people also tend to associate negative feelings with humanoid robots. If you don’t already have one, invest in creating an avatar for your bot that reflects both your brand and user expectations, putting together a friendly and trustworthy face in front of your users. Inbenta offers an avatar catalog to help businesses explore potential options for what your character could look like. 2. Conversation This is the most essential part of any chatbot that intends to be useful. A bot that can’t hold a conversation is just lines of code taking up space in a web server. Here, it’s helpful to remember who your users are, and what kind of etiquette they require from a chatbot in order to initiate a dialog or respond to a prompt. Conversation isn’t just chit chat, it’s the facilitator between a user and what they want to achieve. It smooths transitions between interactions, and increases user confidence. 3. Context & Understanding Beyond the welcome chit-chat, what does your bot actually do? Can it process complex queries? Does it operate on keywords? Does it store in its memory different variables obtained from the conversation? Or does your bot forget what a user says after each input? Context awareness is critical in the ability to maintain an efficient, conversational exchange with users. 4. Dialog The conversation should be driven by user needs. Why do people visit your site? What is the intended conversion? What types of contents and responses should you have prepared? What questions should your bot be asking? What information should it be storing? Try leveraging user personas to direct the flow of conversation around their needs. Just like in real-world dialogs, chatbots should be prepared to change or revisit any topic that suits the user’s needs. The Inbenta Chatbot employs context awareness, a key feature that allows a bot to recognize and temporarily store significant variables in a conversation. Example: 5. Function Conversation is what embeds a chatbot into a user flow, but functions — such as the ability to purchase things — are what make it a buyer flow. Your chatbot needs to accomplish the same tasks as in-store employees: answer questions, process answers, locate items, complete transactions, et cetera. Utility, provided by webhooks, transactional capabilities, and third-party integrations, makes a chatbot a useful, usable piece of technology to consumers. When stripped of its purpose and practicality, a chatbot is nothing more than a gimmick. Some useful functions to include in your chatbot: Resetting passwords Purchases Returns Checking item availability Tracking order status Changing delivery address Checking in-store availability, et cetera. 6. Escalation Today, even state-of-the art AI has its drawbacks. We’re not yet at a point where chatbots can understand customers to the fullest extent in every circumstance. To prepare for those situations, it’s often helpful to hand the baton to a live agent who can take over a conversation exactly where the bot left off. Upon escalation, the Inbenta chatbot delivers conversation history and context to the live agent taking over the chat, establishing a seamless replacement without the need for repetitive information from the user. Remember: anyone can put together a functioning chatbot, but if people aren’t using it, then it isn’t working. And the best way to ensure that customers use your bot is to make it a good experience.   Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company   Let’s get in touch The post Chatbot UX: 6 Ways to Keep Customers Engaged appeared first on Inbenta. Read more »
  • 5 Ways to Use Chatbots for Internal Employees
    Chatbots are a time-saving resource for internal employees whose energy is better spent on meaningful work and productivity. Internal chatbots have the potential to boost accessibility, efficiency, and employee satisfaction in your workplace. Chatbots are easy to use, setup, and deploy. Chatbots, conversational agents, virtual assistants — whatever you choose to call them, automated conversation is more relevant than ever. The growing presence of chatbots on customer-facing websites has resulted in quick and automatic answers to most online inquiries. However, it’s crucial not to overlook the value of chatbots for internal use by employees. To maximize your support performance, it is essential to employ the right tools. When it comes to matching customer queries to the right answers, our natural language processing engine powers the Inbenta Chatbot to achieve 90% efficiency, responding to customer inquiries within milliseconds of hitting the enter key. As a customer-facing tool, chatbots can be highly effective in reducing support costs, increasing customer satisfaction, and boosting the number of successful digital transactions. On the flip-side, can the same artificial intelligence application be used by internal users to advance their company’s goals? Absolutely. In fact, there are many use cases for AI-powered chatbots, with each variant application striving towards one common goal: to improve the experience and efficiency of the user. In other words, to help save time and energy that can be better allocated elsewhere. Here are 5 great use cases that show how AI can be your employee’s best friend. 1. Chatbots streamlining HR support. Decreasing effort and time on task — this is the universal standard of using AI to perform tasks more efficiently. At their most basic function, chatbots help us rapidly complete menial tasks by answering and resolving our simple yet urgent problems, processing queries like “how do I file expense reports?” down to the classic “I forgot my password.” But in this case, information is not always as available as the user would like on her intranet, and when it is, the content they find can be dense and indigestible — solutions can differ from one post to another, the tools may be different from one service to another, and so on. Oftentimes, the user gets lost in the middle of all these variables and turns to customer support in human resources with a dual-consequence: The first user loses her precious time, and The escalated situation now forces a second employee in HR to waste his precious time. To avoid this counterproductive scenario, internal chatbots can provide immediate answers to the vast majority of intentions expressed by employees. Thanks to our advancements in natural language processing, the user can instantly discover the solution that best fits her intent. Layered with dialog flows that outline potential conversational paths, it’s possible for the chatbot to provide personalized, accurate answers to the employee in a matter of seconds. 2. Chatbots facilitating employee on-boarding. If you’re a manager, you know that the word “on-boarding” entails an onslaught of tedious, time-consuming administrative obstacles: HR forms, data entry, tool distribution, and multiple interfaces in which the manager is obligated to enter the same information over and over again — first name, last name, department, etc. In most cases, this is not the most efficient way to log a new employee. Here, chatbots come into play with their ability to control different tools. While you save time by delegating these processes to a bot, sparing yourself the frustration in doing so might be the better point to end with for this particular use case. through a single, user-friendly and accessible interface. By programming the chatbot to register and onboard the new employee by itself using APIs, the manager only has to provide the necessary information to the bot through a simple conversation. 3. Chatbots helping with day-to-day tasks. There are so many tasks that most employees have to do themselves that are low in complexity but extremely time-consuming in aggregate. For example, scheduling meetings across multiple employee calendars, booking meeting rooms, submitting hours, requesting time off, and so on. The collective breaks one takes from work to get organized lays a significant impact on concentration and productivity. However, this is precisely the use case that a bot with natural language processing capability can handle. Here, chatbots can be used to allow employees to significantly reduce the processing time of these tasks by doing it for them. As a result, the affected employee never has to leave their desk or spend time navigating calendars or HR platforms, temporarily removing them from meaningful work. 4. Chatbots providing the source of truth: from taxes to GDPR. Chatbots also come in handy during product development, administrative changes, transitory periods, and any updates pertaining to your employees or workspace. You may have recently adjusted your workflows and policies to adapt to the GDPR (General Data Protection Regulation). As of May 25, 2018, companies that handle (or enable other companies to handle) the data of EU residents are obliged to comply. Naturally, this leads to new internal processes, forms, confusion, questions, and a shortage of timely, useful answers. This is just one example of a big change — just like taxes and administrative, fiscal, and legal changes affecting your your business — that creates obstacles for your employees, and may concern sensitive, critical information regarding their rights, obligations, salaries, and dues. These periods often reflect peaks in HR activity. However, spooling up an influx of new hires in an attempt to create more support bandwidth is not a smart or feasible response. By choosing a chatbot to leverage various knowledge bases, it’s easy to set up an editorial team to prepare content in anticipation of such events. How could it get simpler than that? 5. Chatbots empowering physical robots. Finally, the retro-futuristic vision of robots capable of meaningful human interaction has evolved from science fiction to real-world possibility. Humanoid robots are on the trajectory to become more than a gimmick, and will eventually be used to simplify not only customer interactions, but the workflows of your employees. Seating a patron at a restaurant, guiding a visitor to a meeting room, connecting two employees, giving a group tour — there are endless opportunities in which a chatbot may be embedded into a physical robot to allow employees to thrive in the workplace. Most importantly, the robot-chatbot hybrid amplifies a workplace’s accessibility by increasing the means of interaction with differently-abled individuals. A robot capable of multiple modes of communication would be able to adapt and accommodate to the interacting person’s preferences, strengthening the user’s identification with her surroundings, and helping her understand the workplace as best as possible. These are only five use cases in a world where the application of chatbots are numerous and constantly expanding. The sooner we leverage this evolving technology, the faster we can deliver better results to customers and employees alike while staying on top of current developments that could continue to accelerate business growth in the future.   Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company   Let’s get in touch The post 5 Ways to Use Chatbots for Internal Employees appeared first on Inbenta. Read more »
  • What is the difference between a chatbot and a virtual assistant?
    The VHS and the Betamax, the Blu-ray and HD DVD or more recently the current virtual headset battle between HTC Vive and Oculus Rift. The history of technological development is littered with examples of various formats fighting it out for market dominance. At times, these format wars will dictate what we refer to the new invention as. When purchasing a high-density optical disc we tend to ask for a Blu-ray for example. As artificial intelligence moves out of its winter we are encountering confusion over what to call the intelligent computer programs that communicate with us – chatbot or virtual assistant. Are chatbots and virtual assistants the same? It depends on who you speak to. A school of thought exists which believes there is no difference and that either one could be an umbrella term for the conversational agent. If this is the case then it seems redundant to have two names for the same function. Chatbot is by far the more popular term according to Google Trends. In general, if its primary mode of interaction is through messaging (Slack, Facebook etc.) then you are communicating with a chatbot. There is an argument that the likes of Siri cannot be a chatbot because it exists outside of these channels. But this does not feel like enough of a differentiator. In fact, of more importance is the function of the chatbot (or virtual assistant) that you employ. In this regard, there are some myths surrounding their capabilities which should be debunked. Myth 1: A chatbot is not intelligent enough Some of the most powerful chatbots are equipped with robust natural language processing in order to understand the meaning of an inquiry rather than simply the keywords. Previous bots might have only been able to carry out a limited number of conversations through either hard-coding, wildcard matching of words and phrases or time-consuming keyword training. However, bots powered with NLP are now far more flexible. Unfortunately, many chatbots do not leverage true NLP and are giving chatbots a bad name. Thanks to machine learning, chatbots will continue to improve and will produce higher self-service rates than ever before. Myth 2: A virtual assistant can carry out a wider range of functions While there might be some truth to this now, the gap between what the two hope to achieve is constantly narrowing. In the past, the chatbot could only perform specific tasks such as a password change or information about the weather. Whereas, the virtual assistant was more wide-ranging in what it offered. Thanks to advancements in NLP and machine learning, however, this is changing. Chatbots are now far more diverse and can carry out more functions through their ability to understand natural language. The use of decision trees, for example, makes it far easier to discover the exact intent behind user inquiries, broadening its functionality even further. Myth 3: A virtual assistant is better at remembering context Virtual assistants even now still struggle to remember key information during conversations but chatbots are already proving they can store what you tell them. For example, Inbenta’s chatbot Veronica is able to remember your email address if you provide it to her. If you tell her “My email address is….” then she will retain that information for future use. Therefore, if you were to ask for a demo she would not require you to resubmit it. Rather than debate what we should name them, it is important to recognize how the chatbot (or virtual assistant) will provide the most human-like experience possible by understanding our natural language to the best capabilities. Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company. Let’s get in touch The post What is the difference between a chatbot and a virtual assistant? appeared first on Inbenta. Read more »
  • AI doctors: how artificial intelligence can help medicine
    Around 12 million American adults are misdiagnosed every year by doctors. That is enough to fill the country of Ireland two and a half times over. With an aging population, making the country’s healthcare system more accurate and efficient is becoming an increasing priority. Fortunately, there is a solution. Artificial intelligence is already making great strides in treating patients and also reducing the strain on medical staff. Crowdsourcing diagnoses: Why ask for a second opinion on a condition when you could have a ten thousandth? Human DX uses machine learning to help doctors deal with difficult medical cases by soliciting advice from fellow experts across 70 countries. Physicians can ask questions on the app/website while uploading relevant images of the condition and any related tests. The AI program then calculates all the responses and provides a single report. The potential of Human DX is clear when you explore the rising waiting times in some of the world’s wealthiest countries. Studies have shown that the number of online consultations will increase cumulatively by 25% a year over the next five years, sparing both patients and doctor significant costs and wait times. While providing more accurate diagnoses for patients, Human DX could improve the skills of doctors by tracking their clinical performance and offering recommendations in specific areas. Indeed, many physicians might feel more comfortable taking advice from machines rather than being judged by more experienced medics. AI doctors: detecting diseases Not only can AI help with difficult diagnoses but it can also spot the early signs of many illnesses. Google has already made progress on this front by using machine learning to detect signs of eye diseases related to diabetes using image recognition algorithms. Google’s software can spot tiny aneurysms which can cause blindness by examining photos of a patient’s retina. It is incorporating its diagnosis system in India alongside the Aravind Eye Care System which provided some of the images to train its image parsing algorithms. Google uses the same deep learning technique in its image search to differentiate between cats and dogs for example. In fact, Google claims its algorithm is so accurate that it is on par with that of ophthalmologists. The next step is implementing it to support the 70 million Indians with diabetes and 400 million sufferers around the world.   The technology does not necessarily mean fewer jobs for doctors. Google says its algorithms will perform the screening work which staff struggle to fill, freeing them up for more important tasks. What’s in your DNA? Artificial intelligence will soon allow us to take full control of our health by unlocking the code to our DNA. Despite the significant progress made in healthcare we still do not know what our genome is telling us. Companies such as Deep Genomics are attempting to lead the way by interpreting DNA through a system which predicts how genetic variation affects molecules. In fact, its database can already explain how hundreds of millions of genetic variations can impact our genetic code. A greater understanding of our human DNA means doctors can soon provide personalized information to suit each of us – giving us full control of our bodies. From here, genetic companies such as Rthm can develop tools to understand our genetic makeup and advise on what changes we should make to our daily routines. A more intelligent approach to our long-term health will reduce the strain and resources of our medical staff. Discovering new drugs faster Bringing new drugs to market is both costly and time-consuming. A discovery can take 14 years and cost around $2.6 billion before it becomes available. A major reason for this delay is the need to test the chemical compounds against every possible combination of cells, genetics and any other mutations to ensure it is safe. Machine learning has the potential to reduce these costs by as much as 70% by analyzing the scientific literature at hand. In fact, Benevolent.AI has already identified two potential drugs for Alzheimer’s through this method. Given an aging population and fewer resources for medical care, AI is a solution that ensures we do not simply stick a massive band-aid on the problem but rather properly treat it so it never becomes an issue again. Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company. Let’s get in touch The post AI doctors: how artificial intelligence can help medicine appeared first on Inbenta. Read more »
  • The history of the search engine: from index cards to the AI chatbot
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    How did people find answers before the internet? Even those of us old enough to remember a life pre-web struggle to recall how we did our homework, checked for correct spellings or even resisted the urge to ask those questions we wouldn’t dare ask aloud without the relative safety of a search engine like Google. And yet humans somehow managed to exist before the World Wide Web was created in 1989. Since then, search has improved exponentially to the point where a personal chatbot to help with our most routine tasks is becoming a reality. But how did we reach this point? Before the internet: Searching was far more laborious and in many cases would not even have even taken place before the creation of the search engine. Index cards were first popularized by Carl Linnaeus to classify more than 12,000 species of plants and animals. In the years following his idea, libraries began to rely on them to index their collections. Eventually, libraries settled on the Dewey Decimal System which organized all books by subject, author and title – one which is still in place across libraries today. The first search engine: With the invention of the internet came the first example of what we are familiar with today. But it was not Google, Yahoo or even Ask Jeeves which were the first to introduce a whole new concept to us. Archie was written in 1990 by Alan Emtage and indexed all the file lists of as many public FTP servers as possible to allow users to find and download publicly available files. While it was not on a par with the search technology available today it was indeed better than the alternative – word of mouth. The web directory: For a while, it was regarded as the internet’s most important search engine, but that label did not fit the early versions of Yahoo. In fact, it was considered a web directory that relied on humans to summarize websites with short descriptions and to organize them into categories. Created in 1994, Yahoo became so popular that publishers would delay posting their websites to ensure they would be included. Despite the advancements in search, the Yahoo Directory did manage to survive until 2014 when it was closed for good.   The first web crawler: 1994 also saw the first web crawler released – appropriately titled, WebCrawler. It was the first to index entire pages and became so popular that at one point it could not be used during the day. Natural language search: A search engine that Google arguably owes a lot to, Altavista was a pioneer in many of the online search techniques which we are still using today. Notably, in 1995 Altavista became the first search engine to incorporate natural language technology. Among other achievements it also provided the first searchable full-text database of the web, allowed multi-language search and even translated pages. Altavista’s move away from streamlined search towards a more complex web portal ultimately led to its demise as users flocked to the up-and-coming Google. Google: Which finally takes us to the granddaddy of them all. Google’s success can be attributed to many areas, but its most significant selling point is its famous algorithm which was able to yield within fractions of a second more relevant search results than its competitors. In 1996 when Larry Page and Sergey Brin launched BackRub – Google’s precursor – they realized that their algorithm knew which webpage was the best for a topic based on accumulated links and, more importantly, citations from the most authoritative websites. It was this focus on the relevancy of a website that made Google so popular. Semantic search engines: While Google was able to provide the world with answers to searches instantly, companies were still struggling to do the same on their own websites. The Inbenta Semantic Search Engine was first created in 2010 and was able to understand searcher intent and the contextual meaning behind customer’s searches rather than rely on keywords. Much of this capability was due to Inbenta’s patented natural language processing which significantly improved companies’ self-service rates. Voice recognition: The concept of computers which could understand our voice had been around for the  50 years or so before Apple’s Siri and Google both brought it into the mainstream. Google added “personalized recognition” to Voice Search on its Android phones in 2010 as well as its Chrome browser in 2011. Its English Voice Search now incorporates 230 billion words from actual queries. A Stanford Research Institute spin-off was sold to Apple in 2010 and led to Siri and its cloud-based processing. Ironically, its first offering was far more potent than the version embedded on our iPhones today – it was more intuitive, connected to the web and could detect meaning from sentences more effectively. The artificial intelligent chatbot: Chatbots have existed since Eliza was billed in 1966 as the world’s first ‘chatterbot’ capable of communicating with humans as a psychotherapist would. Only now have virtual agents started to make their mark in the search world by providing customers with information across all forms of social media as well as on company websites. Many of them are powered by artificial intelligence and natural language processing which has provided users with a more personal experience when searching – think of a shop assistant minus the need to step out of your house. One chatbot to rule them all: What is the next step in the search world? Chatbots are now starting to combine natural language processing with machine learning. This combination leads to agents that can provide high self-service rates and improve as it gathers more data. Not only will bots become more accurate but we will soon be able to carry out all our searches as well as any transactions within a single conversation. Regardless of whether it is ordering a pizza, comparing the best energy prices or keeping up to date with the latest in the NBA it will all soon be handled within the same digital space. The developers behind the search engine Ask Jeeves might have had a point when they decided to make a butler the face of their company. Search technology is doing all it can to adapt to us. It will continue to do so in ways that we cannot even comprehend. Inbenta is a leader in natural language processing and artificial intelligence for customer support, e-commerce and conversational chatbots, providing an easy-to-deploy solution that improves customer satisfaction, reduces support costs, and increases revenue. Interested in finding out more? Our team of experts is available to show you how Inbenta can benefit your company. Let’s get in touch The post The history of the search engine: from index cards to the AI chatbot appeared first on Inbenta. Read more »
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