Enterprise Artificial Intelligence – Academic Theory or Ready for Primetime?

Solvetheunsolvable has already explored various aspects of consumer AI and products purporting to leverage AI technologies, but is AI for Enterprise ready for primetime?

Investors aren’t the only ones betting big on Artificial Intelligence, it turns out Higher Education is also investing heavily into the space. With heavy investment in research and development, enterprise level AI seems to be having a rocky start.  Earlier this month Northeastern University allocated $50 million to an Institute for Experiential Artificial Intelligence. This institute will be dedicated to uniting leading experts to solve the world’s unsolvable problems.

“This new institute, the first of its kind, will focus on enabling artificial intelligence and humans to collaborate interactively around solving problems in health, security, and sustainability. We believe that the true promise of AI lies not in its ability to replace humans, but to optimize what humans do best.”


Northeastern President Joseph E. Aoun

This isn’t Northeastern’s first step into the world of Artificial Intelligence and Automation. They already have an Institute for Experiential Robotics that is bringing together engineers, sociologists and other experts, including economists, to design and build robots with abilities to learn and execute human behaviors.  Northeastern isn’t just building Institutes for experts to conduct research, they are making it a priority to prepare their students for success in the age of artificial intelligence. They have an entire curriculum dedicated to what they call, humanics which is a key part of their strategic plan, Northeastern 2025.

Northeastern 2025 Promo Video

“We are building on substantial strengths across all colleges in the university,” said Carla Brodley, dean of the Khoury College of Computer Sciences. “Experiential AI is highly relevant to our mission.”

Though Northeastern is an example of one university betting heavily on AI, they are not alone in their quest to equip students with proper education for the AI-enabled future. In fact, government agencies are getting involved in funding AI in Education. The UK has pledged to invest £400 million in math, digital and technical education through the government’s AI sector deal to protect Britain’s technology sector amid Brexit and an additional £13 million for postgraduate education on AI. In the US, just a few days ago, the National Science Foundation announced a joint federal program to fund research focused on artificial intelligence at colleges, universities and nonprofit or nonacademic organizations focused on educational or research activities. 

The National Science Foundation is awarding $120 million to fund planning grants and support up to six institutes, but there’s a catch. Each institute must have a principal focus on at least one of six themes:

  • Trustworthy AI
  • Foundations of Machine Learning
  • AI-Driven Innovation in Agriculture and the Food System
  • AI-Augmented Learning
  • AI for Accelerating Molecular Synthesis and Manufacturing
  • AI for Discovery in Physics

As universities and governments bet big on the future of AI and education, it underscores the importance of AI on a global scale in the future, but does it call into question the current existence of AI solutions ready to take business to the next level? Utilizing AI and automation will be imperative for corporations to remain competitive and for the advancement of business, but when will the floodgates be swept open, and by who, remains a mystery.

Will you leap into the future and embrace AI now? How do you see the futuristic vision of enterprise AI transform your business? Challenge Solvetheunsolvable with your business conundrum or leave your thoughts in the comments below and let’s explore what AI can do for you. 


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Robotics: Automation or Artificial Intelligence?

The rise of robotics – a long touted seismic shift in human existence, the day an inanimate creature is brought to life.  A scary reality in the minds of many conspiracy theorists, and a reality many tech leaders would have us believe is already upon us.  But how close are we to engineering a robotic race?

“Who controls the past controls the future. Who controls the present controls the past.”

George Orwell, 1984

It’s difficult to not think about physical robots tackling common human tasks when we see the word robotics, but now robotics refers to a much larger application of technology and rising industry. Robotics refers to a focus on creating efficiency and replicating mundane tasks, a world that exists beyond purely physical robots, giving rise to automation bots.

Robotic Process Automation (RPA) is an example of an automation bot operating in a digital world. You may be thinking, obviously it’s automation, it’s even in the name, but what is RPA? Used to perform simple, repetitive tasks, such as data entry, RPA is a programmable “bot” that automates a process in order to free up more time for humans that would otherwise be doing these mundane tasks. RPA cannot be considered Artificial Intelligence as it does not have the ability to understand the implications of the tasks it is performing, or predict future scenarios arising from the performance of these tasks.

Amazon Scout

In contrast, Amazon’s Scout is out on the streets in California, a physical robot, making package deliveries.  This Scout robot may be physical and operate in the real world but similarly to RPA it is another example of automation, lacking human intuition. Just like RPA this Scout robot is programmed to deliver a package straight to your door, removing this repetitive task, lessening the burden on man and machine, but the bot is not capable of modifying the delivery location to the back door under the overhang when rain is predicted, unless the delivery instructions are explicitly programmed to do so.  Far from artificial intelligence, the Scout is simply a machine programmed to automate a repetitive human function.

While individuals commonly mistake robotics as artificial intelligence, it’s important to understand why RPA and delivery robots are not examples of true artificial intelligence. Are they intelligent bots? Maybe. They certainly process and execute complicated instructions and factor many variables, but they lack inherent cognitive function.  Humans are constantly concerned about the demise of humanity as robots are brought to life, but because artificial intelligence still lacks the ability to replicate common sense, the rise of the robotic race will still remain in the halls of science fiction.



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Data Bias on the Daily: Criminal Sentencing- Not all algorithms are created equal

Imagine This…. You’ve been convicted of a non-violent crime, say petty theft. Your legal team decides the best course of action is to take a plea deal. On the day of your sentencing, the judge rejects your plea deal and doubles your sentence. Why? An algorithm says that you are at high risk for violent crime in the future…

You may be reading
this thinking, that can’t possibly be real? But that is an all too real
scenario because of the COMPAS algorithm.


COMPAS, an acronym for Correctional Offender Management Profiling for Alternative Sanctions, is a case management and decision support tool used by U.S. courts to assess the likelihood of a defendant becoming a repeat offender.


The problem with COMPAS, as a ProPublica report states, “Only 20 percent of the people predicted to commit violent crimes actually went on to do so.” ProPublica also concluded that the algorithm was twice as likely to falsely flag black defendants as future criminals as it was to falsely flag white defendants. And therein lies the problem, the algorithm has inherently biased training data due to years of human bias in the courtroom.

COMPAS is not only
biased racially, but it also has bias against age and gender. An independent
study done by researchers at Cornell University and Microsoft found that
because most of the training data for COMPAS was based on male offenders the
model is not as good at distinguishing between male and female as it could be.
They even decided to make a separate COMPAS model aimed specifically at
recidivism risk prediction for women.

But why would COMPAS
separate the data based solely on gender when COMPAS has also shown to have
racial bias? Why are judicial systems still turning to private, for-profit,
companies whose algorithms are known to support racial, age and gender bias?

Turning to these
types of algorithms have long standing implications on human life and our
judicial system. Criminals receiving their sentences in the early ages of
algorithmic adoption should not be test samples or guinea pigs for faulty and
biased algorithms. As Artificial Intelligence becomes more main stream,
understanding the data sets and training methodologies is key to understanding
the results – how is data bias affecting your daily life?


For more information
on COMPAS and ProPublica’s report, please click
here
.

Up next: DNA Testing: Is knowing your heritage worth risking your privacy?

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