At a recent Cisco Collaboration Summit event, a thought-provoking keynote speech was given by James Cham of Bloomberg Beta. This is an early venture capital fund that invests in data-centric and machine learning-related startups. In his speech, Cham pointed out that the ability to perform complex mathematical operations “inexpensively†has inspired recent technological advances in machine learning. "Cheap" mathematics makes machine learning algorithms run faster and cheaper, which in turn promotes cheaper methods of predicting machine learning and product delivery.
Cheaper, faster mathematical operations make AI machine learning possible, which in turn can lead to cheaper predictions. Machine learning is all about predictions
You may ask, "Why is cheap forecasting important and how does it affect our industry?"
Machine learning is actually a prediction mechanism. It tries to predict whether a person has cancer; whether a self-driving car should stop, accelerate, turn or continue driving; whether a person using a web browser might click on a particular ad; which call center agent is most likely to complete a single Sales; the intention of someone to speak or enter; and so on.
Even in the field of communications, the ability to provide accurate forecasts is also a hot capability. Since January, major players in our industry have acquired machine learning companies. This means that they have purchased forecasting technology:
In January, Avaya acquired Spoken and acquired the latter's IntelligentWire technology. IntelligentWire technology uses speech recognition and sentiment analysis to identify pattern conversations to predict troubles that may arise in the future for optimal actions or conversations.
In February, Genesys acquired Altocloud. Altocloud technology monitors real-time digital behavior to predict what the customer wants to do next, whether the customer can buy or abandon, or when to engage in customer intervention (such as popping up a special trading proposal).
On May 1, Cisco announced that it would acquire Accompany. The Accompany technology search network and the characteristics of individuals in the organizational chart and organization of the organization are designed to help users create and maintain "correct" external relationships.
Accompany forecasts corporate organizational structures and human attributes by searching for public resources on the Web. It aims to help accompany the user in creating and maintaining another "correct" relationship within the organization. (Source: Accompany.com, license)
Just last week, 8x8 acquired Mariana IQ. MarianaIQ technology searches for and rationalizes social media channels so that it can clearly identify the channels used by individuals. You can think of it as the entire channel of the contact center, and vice versa. Then use this information and product and personal profile data to actively develop the market according to these individuals’ personal favorite social media channels.
Just as the economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb in their book Prediction Machines: The Simple Economics of Artificial Intelligence, predictions are for almost every business process. Basic input. Better prediction means better information and better decision making. They believe that redefining artificial intelligence and machine learning as a transition from expensive predictions to cheap predictions, or "from scarcity to abundance, is invaluable for thinking about how it will affect your business." Machine learning and its predictions have been From expensive to cheap, we see its emergence, mainly in a quiet, behind-the-scenes way to change our work and lifestyle.
To understand the impact on society and individuals from expensive to cheap, we only need to look at our use of artificial light. According to William Nordhaus, professor of economics at Yale University, the cost of light in 1800 was 400 times that we pay today. As the cost of light declines year by year, we no longer even consider whether to turn on the lights or turn off the lights.
The point is, as predictions become cheaper and cheaper, it will infiltrate every aspect of our work, society, and personal life, and predictions can play a role. To a certain extent, we have already seen this happen. Every time we search the internet, shop online, make credit card purchases, twitter, change our smart thermostats, watch cable shows, make mobile calls, contact call centers, and so on, our data will soon be Machine learning algorithm processing to better predict our behavior and results.
So what did all this bring us?
The economics of making things cheaper often leads to an increase in the value of other things. With cheap predictions, good judgment and reasoning -- what the machine learning system is not good at doing -- becomes more valuable. In my previous career, when I began to use complex thermodynamic, heat, and mass transfer algorithms to simulate petrochemical processes, we often said, "The computer gives the answer after it is programmed, but if If we blow up the factory, what's the relationship?"
Judgment will undoubtedly play an important role in the low-cost prediction of machine learning. This will allow us to adjust machine learning algorithms to achieve better predictions.
Avaya, Genesys, Cisco, and 8x8 all put a lot of money into cheap predictions because it can help business decision makers create high-value results. So far, these "cheap predictions" have focused on contact centers (Avaya, Genesys, 8x8) and collaboration experiences (Cisco). Microsoft's action in this game is also obvious, because it is constantly deploying more machine learning in Office365 and Teams products.
The cheap predictions are in demand, and people who put them out have spent a lot of money. However, the capabilities it supports can and are affecting the way in which each of us conducts business and interaction with many businesses and individuals within our sphere of influence.
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