Why chatbots need a big push from deep learning
Most tech giants are investing heavily both in applications and research, hoping to stay ahead of the curve of what many believe to be an inevitable AI led paradigm shift. At the forefront of this resurgence are the fields of conversational interactions (personal assistants or chatbots), computer vision and autonomous navigation, which thanks to advances in hardware, data availability and revolutionary machine learning techniques, have enjoyed tremendous progress within the span of just a few years. AI advances are turning problems previously thought to lie beyond the realm of what machines could tackle into commodities that are percolating our everyday life.
Tailing the remarkable growth in popularity enjoyed by AI, a new generation of chatbots has recently flooded the market, and with them the promise of a world where many of our online interactions won’t happen on a website or in an app, but in a conversation. Helping turn this promise into reality is a combination of better user interfaces, the omnipresence of smart-phones, and new, state of the art, machine learning techniques.
Perhaps one of the main drivers behind this wave of novel AI applications is deep learning, an area of machine learning that, despite existing for roughly 50 years, has recently revolutionized fields such as computer vision and natural language processing (NLP). Nonetheless, despite its incredible performance, deep learning alone is not sufficient to solve the challenges faced by chatbots. The ability to understand context, disambiguate between subtle differences in language that can lead to wildly different meanings, logical reasoning, and most crucially, understanding the preferences and intent of the consumer, are just a few of the many challenging tasks a system must be able to perform in order to sustain a conversation with a human.
The ability to answer complex questions using not only context, but also information beyond the confinements of the dialog, is indispensable for building truly powerful chatbots. To answer questions effectively, the bot needs to rely on information that was either shared previously in the conversation, or even within other conversations between the bot and the consumer. Moreover, business goals and the intent of the consumer can influence the kind of response the bot will give.
If a modern conversation engine hopes to go beyond answering simple, one-level questions, it must blend together the most prominent techniques emerging from the field of deep learning, with solid statistics, linguistics, other machine learning techniques, and more structured classical techniques such as semantic parsing and program induction.
The first stop in building an intelligent conversational system is data. While we live in an era where endless streams of data are constantly being generated, most of it is too raw to be of immediate use for machine learning algorithms. In particular, deep learning is notorious for its need for vast amounts of high quality data before it can unleash its true potential.
Unsupervised Learning, the subfield of machine learning devoted to extracting information from raw data, unassisted by humans, is likely a promising alternative. Among its many uses, it can be utilized to build an embedding model. In plain English, these techniques allow one to represent their data in a less complex form, allowing for easier discovery of patterns.
