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February Dinner Meeting Recap E-mail
Written by George Orlin   

Artificial Intelligence: Driving Exponential Innovation

Have you ever thought to yourself: "What would it be like for someone from the 1700's if they were dropped into 2019?"

AI Dashboard


Tim Urban, American futurist and creator of the inquisitive and cheeky blog Wait But Why, asked himself the same question. His answer? They would simply die, overwhelmed with the enormous amount of progress. This concept would become known as DPU, or "Die Progress Units": the amount of innovation and progress that needs to occur before a previously unexposed audience simply dies upon being exposed to the innovation.

Artificial Intelligence: The driving force towards the next DPU

 

AI Attendees


In our most AITP dinner, Alex Vayner, Data Science & Artificial Intelligence Leader at Capgemini, explored what we could expect from the next "DPU" that would likely occur in the dramatically short time between now and 2030, primarily due to the explosion of progress in artificial intelligence and cognitive automation. Alex described a near future where AI would augment and enhance human capabilities, from driving vehicles to making better business decisions. Many were left asking the question, "what really is the magic behind AI that could bring these ideas to fruition?"

Alex's explanation was that AI is a collection of mathmatic and statistical algorithms aimed at answering specific questions, from understanding natural spoken language to identifying an emotion in an Instagram image.

AI Instagram

 

Additionally, the reason that AI is becoming more prevalent in recent years, is due to a perfect alignment of several factors that have set the scene to turn mathematical theory from the 50's into a reality in 2019. These factors include:

AI Factors

Over the next 50 years, we will likely experience incredible progress in the realm of AI. Imagine machine generated New York Times bestsellers appearing in 25 years, robotic surgeons in 35 years, and computer-automated math research in 45 years. Our ability to innovate is exponentially increasing with the power of cognitive computing.

Sounds great, so how does this all work?

Under the hood of AI

Today, for AI to deliver the right answer to our questions, it will need carefully curated algorithms and an enormous amount of training data. These algorithms are first represented in code using high-level programming languages such as Python, Scala, and R. The algorithms are then deployed onto powerful compute hardware such as GPUs and refined using very large sets of annotated training data. As the GPUs power through millions and billions of records of this training data, the machine "learns", and the confidence level of the algorithms is continuous measured until the algorithms are able to consistently annotate unannotated data correctly.

This process is typically facilitated by a data science team, referred to by Alex as a "POD". The POD consists of four different experts, with specific responsibilities necessary to produce a meaningful AI solution. A typical "POD" might look like this:

AI Pod

Some of the titles in this "POD" are probably familiar to you. In fact, countless economic sources state that they are amongst the most desirable and in-demand jobs in 2019.

About AITP Atlanta

Does emerging technology fascinate and inspire you? Are you looking to learn more about high-tech including artificial intelligence, blockchain, and cybersecurity?

Then we have some great news… AITP Atlanta is shaping up to be the premier forum of discovery and innovative thinking for local technologists in 2019. We’d like to invite you to join a tight network of professionals, just like you, on the journey to the cutting-edge of the possible across the technology industry.

AITP Cityscape

Association of IT Professionals (AITP) is the leading association for technology professionals, students and educators. Join us to build your professional network, strengthen your technical knowledge and soft skills, develop a personal career path, and keep current on technology and business trends. Be part of the community that continues to push technology forward and join thousands of other tech professionals as an AITP member.

 

 
January 2019 Dinner Review E-mail
Written by Programs   

What is the deal with Robotic Process Automation?

 

RPA

In our most recent AITP meeting, Mary Elizabeth Hooper, Director IT - Innovation, Robotics (RPA), Customer View, Adoption and Service Measurement/Reporting at Synovus helped demystify RPA.

In a nutshell, RPA is like a macro. Many of us have used or built macros in our careers; oftentimes through tools like Microsoft Excel. Similar to the Macros of years past, core RPA technology is not thinking, is not learning, it is only “doing”. Core RPA tools follow a set of rules to perform a sequence of automated actions with the goal of acting like a human. There are certain forms of RPA that are more advanced, automatically enhancing rules engines based off of “learning” from the continuous executions of actions and observations of outcomes. A good example are many chatbots; chatbots, while appearing fascinatingly intelligent, are often simply guided by a set of rules initially programmed by a human or adjusted by an algorithm.

At a certain point, when the technology begins to “reason”, navigating the “gray-area” in between rules, it transcends from being an RPA solution to a Cognitive Automation solution. These solutions support many advanced capabilities including natural language processing. Google Home, Alexa, and Watson are all examples of Cognitive Automation solutions.

While this makes sense, what is the value to a business? Mary shared her perspective garnered from her experience at Synovus:

Large businesses, especially those that grow through mergers and acquisitions, are constantly faced with the challenge of wrangling their data. Oftentimes, key financial and company data lives in different forms in different systems; many of these systems are legacy without effective mechanisms (like APIs) to access that data. In this state, business analyst resources have to invest significant time to manually query, access, and re-key data to create the necessary aggregate reports for the business. To combat this manual requirement, the IT teams for these large businesses are often asked to undertake the difficult task of integrating these systems through a complex programming effort. So how does RPA help?

RPA allows those manual business analyst tasks to be automated through a series of steps and rules. Query a database? Copy a record? Enter it into a spreadsheet? All automated into the RPA equivalent of a macro. The end result is an automated workaround for having to deal with legacy systems, saving the time and expense of having human resources perform those tasks manually.

In a perfect world, where system effectively “talk” using standard open APIs, the need for basic RPA would fade. However, we are a long way away from that utopian view. As long as businesses are continuing to rely on disparate, siloed systems of record, RPA will continue to be a valuable tool for navigating the chaos.
RPA

 

 

 
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