Whether you ordered a custom-made gift on Etsy, stayed at an Airbnb on short notice, completed a chore with TaskRabbit, or hailed a car to your home in a few minutes with Uber, sharing economy platforms have become ubiquitous. Because chatbots are perfectly positioned to provide personalized, niche experiences tailored to customers' tastes. Using the example above, a person who sells outfits on Etsy might offer a fashion assistant who can provide tips and how-tos for pulling off a certain look. Because the sharing economy connects customers with independent individuals -- freelancers, everyday people looking to sell excess items, rideshare drivers, etc.
However, these new algorithms also made it necessary for legitimate digital marketers to dig deeper and put more effort into quality in terms of technical SEO, content development, and link building. Today, only legitimate editorial links, which take significant time and effort to earn, will produce safe, long-term results. Instead of a static formula, it utilizes user experience, big data, and machine learning to produce results that meet user needs more precisely while learning and improving on the fly. RankBrain is Google's machine-learning artificial intelligence system that helps process its search results, which uses an entirely new way of processing queries according to Greg Corrado, a senior research scientist at Google who is involved with RankBrain.
While every interaction essentially comes down to a natural language conversation, different platform capabilities and usage scenarios dictate which platform makes the most sense for your brand. Voice generally works best for quick interactions, such as tracking a flight. Incorporating natural language processing (NLP) into your assistant depends on its use case. For example, we saw strong upticks in usage of Kayak's Messenger bot when we allowed people to answer questions with simple touch replies.
Take, for example, Artur Filipowicz, an AI researcher at Princeton University who's been trying to develop software for autonomous vehicles. Now, DeepMind can beat just about any top score on any Atari video game. Privately funded organization OpenAI has taken the world of video game-based AI development to new levels, with a piece of software it calls Universe. The future of video games in AI development is rich with potential, and we're just starting to explore its full capabilities.
While science fiction often portrays AI as robots with humanlike characteristics, AI can encompass anything from Google's search algorithms to IBM's Watson to autonomous weapons. The big data infrastructure, the deep learning models, and everything else exist to serve the data, not the other way around. AI provides the large-scale analytics needed to extract meaning and benefit from big data, while big data provides the knowledge needed for AI to continue to learn and evolve. Its investments in AI and big data infrastructure are essentially a means to get you to share more data, and then analyze it to sell ads against it.
The voice ecosystem is booming! Voice is even disrupting search, with 60% of people using voice search last year. Technology is finally able to create innovative voice first experiences and an increasing number of consumers are adopting voice technology, which is further pushing innovation within the sector! We decided to dive deeper into the voice ecosystem and produce this world voice landscape, so you can know who is who in the world of voice!
But there is a bright side to this, if companies are flexible: Industries that understand they need to evolve and adapt to new technologies will be able to quickly replace "killed jobs" by placing talented individuals elsewhere and focusing on more important business activities that require more creativity and "human input". It is widely known that bots are faster and more precise than humans at some tasks; human errors are easily replaceable with intelligent chatbots which can remember actions that have been made a long of time ago and can offer a more accurate, personal and rapid service to multiple clients at the same time. It is thus a no wonder the chatbot market is a great opportunity to develop tools to help businesses grow and manage their activities more efficiently. Yes, they will inevitably kill jobs, but as long as industries move quickly and are flexible, they will be able to place talents elsewhere in services that need to supervise, maintain and work with chatbot tools.
Since then, the fake news phenomenon has created the means for people (including public leaders) to dismiss reports of their wrongdoings and infuse otherwise legitimate political debates with falsehoods. Other AI systems being developed to identify fake news use Natural Language Processing (NLP) to conduct a complex series of analyses on news items. Algorithms written specifically to identify fake news might compare the ways in which different sites cover certain news events and how a lesser-known site's coverage stacks up against mainstream outlets, as well as dissect elements such as context and location. Platforms, including social media, enable users to flag posts as fake news.
To help business and IT executives evaluate emerging technologies and their potential impact on the digital transformation of their organizations, Forrester recently published "Top Technologies for Digital Predators, 2017," a detailed analysis of 15 emerging technologies with a wide range of disruptive potential and time-to-impact. The landscape for this emerging technology is expanding rapidly to include a wide range of chatbots, virtual agents, robotic process automation, and other digital assistants. As minders of internal processes, Intelligent Agents also promise to reduce costs, improve productivity and optimize all types of business activities. Augmented reality (AR) overlays digital information and experiences on the physical world while virtual reality (VR) creates a new interactive digital environment.
Luckily, progress in machine learning and natural language processing is giving new meaning to "conversational" devices. She talked about a machine learning technique called Word Embeddings, where vectors are used to represent words in 300 dimensions, which introduces context into artificially intelligent systems. Amazon Lex (AWS's service for building conversational interfaces into apps), works to optimize chatbots around intent of use, helping companies build bots that can anticipate your next move. What's next is for us to think critically about where machine learning techniques make the most sense.