adv

The AI revolution: Is the future finally now?

The AI revolution: Is the future finally now?Over the last several decades, the evolution of artificial intelligence has followed an uncertain path–reaching incredible highs and new levels of innovation, often followed by years of stagnation and disillusionment as the technology fails to deliver on its promises.

Today we are once again experiencing growing interest in the future possibilities for AI. From voice powered personal assistants like Google Home and Alexa, to Netflix’s predictive recommendations, Nest learning thermostats and chatbots used by banks and retailers, there are countless examples of AI seeping into everyday life and the potential of future applications seem limitless . . . again.

Despite the mounting interest and the proliferation of new technologies, is this current wave that much different than what we have seen in the past? Do the techniques of the modern AI movement–machine learning, data mining, deep learning, natural language processing and neural nets–deserve to be captured under the AI moniker, or is it just more of the same?

In the earlier peaks of interest, the broad set of activities that were typically bunched together under the term ‘AI’ were reserved for the labs and, if they ever saw the light of day, they were severely constrained by what the technology of the day could deliver and were limited by cost constraints.  Many of the algorithms and structures central to AI have been known for some time; rather, previous surges of AI had unrealistic expectations of immediate consumer applications that could never be accomplished given limitations of the data and techniques available at the time.

Within the last five years however, the combination of enormous amounts of data and improvements to database technology to effectively utilize it, along with increases in computer horsepower to process it, have enabled AI to move from mainly scientific and academic usage to enterprise software usage, becoming viable as a mainstream business solution.

This time around, the AI movement seems to have tailwinds in the form of a few critical enabling and supporting factors:

    Technology and computing horsepower driving AI capability at the right (aka low) price point

    The availability of platforms from major players in the field like Google, Microsoft, Elon Musks' OpenAI, Amazon, etc.
    Mainstreaming of practice-- slowly building critical mass of practioners who leverage these platforms
    Mainstream customer interest and demand reflected in real world 'everyday' use cases-- data security, computer assisted   diagnosis in healthcare, fraud detection, purchase prediction, smart home devices and more
    Increasing mass of data that is waiting to be exploited, which cannot be done solely by human means
    Mainstreaming Changed customer expectations from what is doable using technology, which is further driving innovation in a secular manner (e.g. Alexa)

As the tide is turning for AI, innovation-and technology-driven corporations and their leaders–think IBM, Yahoo, Salesforce, Uber and Apple–have become believers in the power of AI and are willing to commit long term funds to this pursuit. The desire to inject new technology into their operations to drive corporate efficiency or improve workflows (both customer and backoffice) has convinced many large corporations that this new iteration of AI is worth investigating and worth investment through acquisitions and investments in startups that innovate independently. 

In addition, tech heavyweights Google, Facebook, Amazon, IBM and Microsoft recently joined forces to create a new AI partnership dedicated to advancing public understanding of the sector, as well as coming up with standards for future researchers to abide by.



Comments