Preparing for Artificial Intelligence

What Should You Know About Prepare.Ai?

We recently announced our first annual conference at Prepare.Ai and are busy assembling the agenda, speakers, and presentations. A few friends and colleagues have asked me to provide a bit more detail on the goals and objectives of our organization.

Artificial Intelligence is expanding in capability and influence at an astonishing rate across virtually every industry. Like any technology that can do things faster, better, and cheaper, this is an exciting development. This means new and improved products and services for all of us. But the speed and scale of AI is such that some long-standing norms are facing disruption. At the Prepare.Ai Conference on May 8, 2018 in St. Louis, we are gathering thought leaders from industry and academia to unpack these opportunities and challenges, and then explore what each of us can do to prepare for this bold new future.

Trending AI Articles:

1. Text Classification using Algorithms
2. Regularization in deep learning
3. An augmentation based deep neural network approach to learn human driving behavior

The Opportunities

Machines have historically improved our standard of living through the automation of human muscle. Artificial Intelligence is now enabling the automation of human cognitive tasks.

Tasks that involve prediction, pattern recognition, visual perception, language, and combinations of all of the above are increasingly being added to the resumes of AI systems.

Types of AI

Perhaps the most prominent example of applied AI in the news today is the sensor-laden, self-driving vehicle. Once it emerges from the numerous pilot and testing programs ongoing already, it will allow commuters to more safely and efficiently traverse city streets while unlocking new time for both productivity and leisure.

In the healthcare arena, some AI algorithms are diagnosing diseases with greater accuracy than a panel of doctors, while others are serving to personalize medicine for a more relevant, individual fit. See the article The Promise of AI in Healthcare written by our Board President, David Karandish.

The AI that I am most familiar with is natural language processing (NLP). David and I also work together at the company he founded, Ai Software, with a team of data scientists, designers, and developers building “Jane,” a virtual team member that will provide access to all of a company’s information in the simplest way possible — through natural language chat. Once Jane has access to a company’s data and applications, the appropriately authorized users can access and inquire 24/7/365 from any device, all without interrupting one of their team members.

Jeff Bezos, CEO of Amazon, says this about the potential of AI: “We are now solving problems with machine learning and artificial intelligence that were…in the realm of science fiction for the last several decades…it really is an amazing renaissance.” (See amazon-jeff-bezos-artificial-intelligence-ai-golden-age.html)

Jane, a Virtual Team Member by Ai Software

The Challenges

By its very nature, technology has disrupted the status quo over and over throughout history. So this is certainly nothing new, but disruption always poses challenges. Let’s zoom in on a related example in textiles and clothing manufacturing. At first, this was a labor-intensive process, where workers would manually sew cloth into a small number of high-cost garments. When the power loom was invented, however, entrepreneurs could earn more money with less labor, and a large number of those manual jobs were eliminated. …But the story doesn’t stop there. In response to higher productivity, better quality, and lower prices, consumer markets began to demand more clothing. Soon, production would far outstrip pre-automation levels, and new high-wage jobs would be created, such as: clothing designers, marketers, machinists, business managers, maintenance professionals, and so on.

Technology Disruption Example: Textile Manufacturing Jobs

This same pattern has taken place faster and faster since the first industrial revolution. Each time, the eliminated jobs were replaced by new jobs, often filled by younger workers who had learned the newly desired skills in school.

The speed of disruption from artificial intelligence today is automating many tasks at a faster rate than previously observed, threatening to strand some mid-career workers in obsolescence. The reach of disruption is also noteworthy, transforming the nature of select tasks across all sectors and industries.

How Can We Help?

I believe that history has already shown us the appropriate response to changes and challenges such as these: Adaptation. The prevailing paradigm that we are faced with is continuous innovation, and we must respond with continuous, data-driven learning.

If AI is the way to competitively improve products and services in a global marketplace, we must embrace it. If AI encroaches on our values and institutions, we must harness and control it. If AI causes technology cycles to quicken beyond once-per-generation, we must reimagine our once-and-done educational system. If AI’s are working among us, we must learn how to collaborate best with them.

To realize this vision, we need grassroots movements. Communities of people exchanging ideas at the water cooler, at coffee shops, and at startup incubators. Courageous experimentation with new AI technologies and business models. Open events, meetups, informal lunches, and social media conversations. The audacity to follow your personal curiosity.

The Prepare.Ai Conference

We want Prepare.Ai to be a driving force in this movement, helping individuals and businesses to adapt and prepare.

Our annual conference will be the centerpiece of this effort. Attendees will be a diverse group of professionals, developers, scientists, executives, professors, and students from all walks. Through the event, we will foster a regional and national hub to broker the import and export of AI ideas, relationships, intellectual property, resources, data sets, case studies, academic findings, and industry leadership.

One unique aspect of our conference is that each session will be tagged on a sliding scale between technical and business tracks — so you can get access to the right content at whatever level you care about. Because of the cross-cutting and horizontal nature of AI, both tracks are probably relevant to you and your organization.

Planned Topics Include:

  • Machine learning / Deep learning
  • Natural language processing
  • Computer vision
  • Forecasting and predictive analytics
  • Data science
  • AI software & hardware tools
  • Specific case studies and business applications of AI

If any of the issues mentioned in this article strike a chord with you, I think you should be there on May 8 to take part in the discussion and make a contribution. I hope to see you there!