One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Many global enterprise companies are benefiting from deploying useful chatbots for customer service, marketing, human resources, communications, and scheduling. Customer service chatbot applications are the most popular, followed by using chatbots for marketing purposes. Many companies using chatbots for marketing are finding them effective in personalizing the experience and improving sales results. Another place to use a chatbot for enterprise businessefforts is in human resource management as a recruitment tool. Chatbot technology can be used effectively to communicate with both the public and employees.
Artificial intelligence (AI) is becoming big business, with all kinds of fascinating opportunities. Growth has been extraordinary: in 2015, global AI revenues were $126 billion, and last year revenues were $482 billion. The prediction for 2024 is that revenues will top $3.061 trillion. Advances in AI are making it possible for computers to take on more tasks that were formally done by humans. While this trend is creating greater efficiencies, it is also increasing the degree to which people feel that they are talking to a wall.
Enterprise search has been stuck in the 1990s for two decades now. It's hard to believe search hasn't kept pace with the explosion of data. The two must go hand in hand. Yet many of us go to work every day at big companies with complex knowledge management systems and fat IT budgets. We sit down, make a coffee, and before we know it, we're transported back to 1997 whenever we need to find a document.
When you're creating a chatbot, your goal should be to make one that it requires minimal or no human interference. This can be achieved by two methods. With the first method, the customer service team receives suggestions from AI to improve customer service methods. The second method involves a deep learning chatbot, which handles all of the conversations itself and removes the need for a customer service team. Such is the power of chatbots that the number of chatbots on Facebook Messenger increased from 100K to 300K within just 1 year.
We can describe a chatbot as a computer program that conducts a conversation in natural language via auditory or textual methods, understands the intent of the user, and sends a response based on the business rules and data of the organization. Another way to describe chatbot programming is the concept of "micro-engagement," or technology designed to communicate with customers and prospects at various intervals and via multiple channels in order to drive business interactions. Whatever the digital classification, it's important for boards of directors and C-level executives within the insurance industry to understand that chatbots are an increasingly effective way to improve business processes -- but are not a panacea. Roughly 65% of customer interaction can now be automated, and in order to maximize their effectiveness, chatbots must be wed to a comprehensive communications process that also includes humans (who can step in at the appropriate time). Being able to extract information from an insurance claim is a fairly complex task that demands a human component.
This article is part 1 of a series sharing the initial results and directions of Omdena's AI challenge on PTSD treatment with 34 engineers and enthusiasts collaborating. Millions of people suffer from PTSD around the world due to various kinds of traumatic events. Professional help is difficult to find when needed in particular areas, which makes it sometimes impossible for the patient to overcome the trauma they live in. This is why Christoph von Toggenburg reached out to Omdena for leverage AI and community collaboration. Before we dive in deeper let's first understand what PTSD is and how professional psychiatrists treat it.
The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."
Some experts believe these chips will play a key role in the race to create artificial intelligence, potentially shifting the balance of power among tech companies and even nations. They could feed the creation of commercial products and government technologies, including surveillance systems and autonomous weapons. Google has already built such a chip and uses it in a wide range of A.I. projects, including the Google Assistant, which recognizes voice commands on Android phones, and Google Translate, which translates one language into another. "There is monstrous growth in this field," said Cerebras's chief executive and founder, Andrew Feldman, a chip industry veteran who previously sold a company to the chip giant AMD. New A.I. systems rely on neural networks.
AI seems to be well on its way to becoming the most overused buzzword of the tech industry, but don't be put off by the hype. Some fintech companies in Asia are actually making use of natural language processing or machine learning for detecting fraud and making investment decisions. I recently interviewed two CEOs--Simon Loong from the Hong Kong unicorn WeLab and Jianyu Tu from MioTech--to better understand some of the recent developments in AI in Asia's fintech industry. Philippe Branche: First, could you describe your company in a few words? Simon Loong: WeLab is a fintech company providing seamless digital financial services.