Posted on

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. 

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.
Posted on

Artificial Intelligence: The Future of Healthcare

What’s more important to parents than the health and well being of their children? Not much, if anything. So imagine this… your child could have a disease that an Artificial Intelligence enabled iPhone app would diagnose more than a year before doctors and all you have to do to find out is take some photos. Sound too good to be true? With the power of Artificial Intelligence, alternative health care tests, such as this one for children, could literally be in your hands right now…

A post from Government Technology explains:

“CRADLE makes use of artificial intelligence to look at baby pictures taken with a flash and pick out instances of white eye. When tested on 50,000 images of 40 children, half of whom had been diagnosed with an eye disease, the technology was able to identify white eye in pictures taken up to 1.3 years before the child was diagnosed.”

BaylorProud

This non-intrusive, ultra- convenient Artificial Intelligence enabled technology is pushing the boundaries of digital health diagnosis, and this is just one example. In addition to convenience, your data privacy is less at risk as the app analyses the photos right on your device, reducing latency and increasing privacy by not uploading to a server. This type of technology, when developed further, could be very useful to detect a variety of health issues.

In fact, there are other apps that are already utilizing AI to address a host of mental and physical health concerns. Some of these health applications range from AI chat bots programmed to treat mental health all the way to AI-enabled apps that can help women track and predict fertility. Investors are betting big on these AI-enabled health apps with millions in funding being pumped into these companies of the future.

The
future of AI-enabled apps is certainly bright. Wearable tech, like FitBit or
Apple Watch, when equipped with built-in Artificial Intelligence, could one day
utilize personal health data to identify diseases such as cancer in the very
early stages. Apple Watch
is already able to use AI to identify
irregular heart beats known as paroxysmal atrial fibrillation (Afib) with 97%
accuracy.

Image result for healthcare cost rising

With the continuous rise of healthcare costs, for individuals and the country as a whole, consumers are looking towards AI for more affordable self-diagnostics. This is evident in more than AI health apps, but also in many at home health kits such as, EverlyWell and QuestDirect, stay tuned for our Data Privacy Series article on at home diagnostics coming soon.

Artificial Intelligence is improving the healthcare space at rapid speed and the technology is only getting better. So the question that must be posed is:

Could AI be the key to solving the $760 billion healthcare crisis?

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

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.

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

Data Privacy Series

When’s the last time you read the terms and conditions before clicking “accept” as you downloaded the hottest new app to your smartphone? Do you really know what companies are doing with your data from your search history, the pictures on your phone, or even your personal health records?

How our data,
personal details and digital patterns, is being used by the scores of apps,
programs, and devices we interact with on a daily basis remains a mystery. When
we click “Accept” on the terms and conditions page, usually in a
hurry, we are blindly 
“choosing” to opt in to whatever data collection and privacy
infringements the developer has built into the technology. What’s more, most
companies use vague statements on their websites regarding what they are doing
with your data and even threaten to impact your service if you decide not to
share your data.

For example, this snippet comes directly from Nest’s FAQ’s:

Nest FAQs

How important is that app or device?  Is it worth signing over your digital rights?

Join the
SolvetheUnsolvable team this month to explore how private your data really is…

Facial Recognition,
friend or foe? Family Heritage Mapping, a key to the past or losing control of
your future?… Who else is checking in on grandma? The hidden dangers in Smart
Home technology. Are you using your cell phone, or is it using you?

Check back in on
Wednesdays this October to learn more about data privacy…

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

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

Data Bias on the Daily: Is AI hindering her job search?

The gender pay gap and women’s representation in leadership roles continues to captivate headlines but what action is really being taken… It’s time to take a journey through the application process for a young adult female, let’s call her Mira, looking to land an interview in a Science, Technology, Engineering or Math (STEM) focused corporation…

A large part of Mira’s job search is online, where she will turn to various social platforms to seek out new opportunities, and depending on the channel she picks, she will be shown job advertisements that ultimately will be based on biased pay per click (PPC) and purchasing algorithms.

Advertising a STEM job across various social networks reached 20% fewer females than males, even though it is illegal to gender bias in both the US and Europe.

Management Science Study

Since advertising algorithms are designed to optimise advertising spend, and maximise reach, less potential female candidates were exposed to the ad and therefore did not get the opportunity to apply. To put it simply, the algorithms are biased against female candidates due to the higher cost of reaching them, even if ultimately the female candidate would be a better hire and take less resources to train.

Despite all of these challenges and biases facing her, Mira chugs along and finds a job posting in her field that captures her attention. Upon reading further into the job and the company, she is both consciously and unconsciously influenced by the language used to describe the role which will determine her next step. The STEM industry, particularly, is imbalanced due to a lack of women being trained and therefore is an industry that remains largely male-dominated. This transfers into the biased language used in the industries’ job listings, which in turn biases the data the job board algorithms are trained on.

A University of Waterloo and Duke University study showed that male-dominated industries (STEM industries) use masculine-themed words (like “leader”, “competitive” and “dominant”) which when interpreted by female applicants deteriorated their perception of the “job’s appeal” and their level of “belongingness” even if they felt able to perform the job. Above this, it is proven that a female will only apply for a job if she fulfills 100% of the criteria, whereas males will apply if they feel they fulfill only 60%.

Determined as ever, Mira eventually finds a job description and company she feels confident about, she submits an application. Her CV and cover letter are parsed and ranked alongside other applicants, male and female. Each success factor identified within the words Mira has used, like her programming skills, is weighted according to what has been historically successful in the past for that particular company.

In the age of Artificial Intelligence, past hiring decisions are used to train algorithms to identify the best suited candidates for the company. The issue with biased data is that even if gender is excluded from the application itself, the gender distribution of the training data may be strongly imbalanced since the industry has been historically male-dominated. This means that even if Mira gets to the point where the hiring company or job board puts her resume through their fit algorithm, she still may not receive the interview based on the inherent bias in the program.

If you’re not convinced by Mira’s journey, here’s a real life example: Amazon’s experimental hiring tool was used to screen and rate resumes based on historical hiring patterns. To date, a majority of Amazon employees were males and inevitably the system taught itself to penalise female resumes, with far greater efficiency than a human.

Still unsure if data bias is
perpetuating gender bias in STEM? Check out these articles from others in the
industry:

How Unconscious Bias Holds Women Back

Mitigating Gender Bias

AI Is Demonstrating Gender Bias

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

Chatbots, Influencer Bots, Gaming Bots, Oh my!

As it becomes
increasingly difficult to get the attention of executives and consumers through
email and advertisements, many companies are turning to Artificial
Intelligence. But the real question remains, what are the ethics behind
corporations using bots to promote their goods and services?

Chatbots have shown
real ROI to many businesses and at times have proven more effective than an
under-trained salesforce. An
Oracle survey has shown that Chatbots could save $174 Billion across
Insurance, Financial Services, Sales, and Customer Service. With vast
abilities, including the ability to chat with actual human dialogue it’s no
wonder companies are so eager and quick to turn to this advancement in AI.
Should a company be ethically responsible to share with consumers that they are
chatting with a bot? Is this on the consumer to “beware” of bots? Do
we as a society care? Should we care?

Influencer Bots, like LilMiquela and Shudu, use CGI and are run by companies specializing in Artificial Intelligence to blend the lines between reality and robot. These two influencer bots have amassed almost 2 million followers and “LilMiquela” is touted as “Musician, change-seeker, and robot with the drip💧💖” in her Instagram Bio. These bots are influencing the purchases we make, the culture we enjoy and now even the music industry. Lil Miquela’s profile is at least honest that she is a bot but Shudu’s bio vaguely states: “The World’s First Digital Supermodel.”

In a world where social media is influencing so many consumer purchasing decisions, especially in younger demographics, is it even ethical to create an influencer bot?

Gaming bots, when
used as intended, can often enhance the gaming experience. For example,
Fortnite has upped their bots’ ability in order to maintain an enjoyable
experience for new comers in their vast online community. However, most gamers
know that bots have often been abused since the idea of using an algorithm to
replace a human online became a reality. Many have used the bot technology to
their advantage and there are companies out there doing something about it.
Niantic, creator of one of the most popular cell phone apps ever, Pokemon Go,
has been extremely strict on bots and gamers trying to cheat the system, going
so far as to take
legal action.

With bots becoming
one of the most common Artificial Intelligence interacted with daily we need to
start questioning the ethical implications. How are AI empowered bots improving
our daily lives? What are the implications of influencer bots like Lil Miquela
and Shudu on the future of our society? How aware are you that Artificial
Intelligence is impacting your purchasing decisions?

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

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?

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

Solved: Dismantling the Silo

We are living in a world that is obsessed with connectivity yet so many large corporations are still working in silos. How can a corporation become connected and move towards Industry 4.0 if their data and systems are trapped in silos? This is a problem many corporations, big and small, face today. In order to understand and address the problem, first we must understand the silo. There is a solution, there is a path forward and Artificial Intelligence can lead the way.

Silos are created when information, goals, tools, priorities and processes are not shared with other departments and this pitfall becomes enforced by corporate culture. An effort to achieve the lowest overall cost and best functionality for different departments, or in the case of some manufacturers the same department in different plants, has created disparate data. Many systems are programmed to not function well together or only function in a stack but there is technology out there dedicated to dismantling the silo.

Executives often get into the trap of thinking the only way to advance into Industry 4.0 is to update disparate systems and increase capital expenditures. What many execs and people in all corporate functions do not yet understand is that Artificial Intelligence (AI) can be the systems connector. The entire premise of AI is built on the notion of interconnected information that may have previously been thought to be unconnected entirely. It is entirely unnecessary for a corporation to increase CAPEX when working with the right AI provider.

In order to get the most accurate picture of underlying issues within the corporation, AI must be able to connect to a vast amount of data from many different silos. This doesn’t mean that you have to dismantle the silos, you just need the right AI connector…. Let Artificial Intelligence be your Silo Dismantling Agent.

Click Here to Learn More

Posted on

Data Bias on the Daily: What’s in your Amazon Cart?

With multiple billions of packages shipped per year, and even
more billions of items purchased, it’s no
wonder that Amazon is a household name. In the time it takes you to read this
article, an estimated 100,000 items will be purchased on Amazon. But how many
of those purchases will be impacted by data bias? It’s likely every single one.

Have you ever
stopped to think about the algorithms behind those convenient home page
suggestions, people also purchased, and related to items you viewed? Maybe in
passing or idly but likely not in detail. In this post we will be exploring
just how much one quick search on Amazon can be littered with bias.

Bias can end up in
your shopping cart in many different stages along the way. It’s safe to assume
that Amazon wants to maximize it’s bottom line, even though they claim they
want to provide the consumer with a great deal. When the engineers set to
writing shopping algorithms at Amazon corporate, they have to add parameters
that are computable and achieve a certain goal. With this in mind, they are
likely adding the bias that they would like to increase Amazon’s bottom line
(aka, profitability of your purchase). Let’s apply this to an example search.

Perhaps you’ve been
searching for storage solutions for your messy closet. The moment your results
appear for “closet storage” you are encountering data bias. Exhibit
A, the screenshot below:

You’re looking at the first instance of bias because a majority of the page is showing you “sponsored” results. “Funding Bias” is written into many algorithms all over the internet and is the reason why sponsored items always show up first. These brands: JYYG, TYPE A, OUMYJIA and JEROAL are paying to bias your search. This is one of, if not the most common search bias encountered but we see so much “sponsored” content that, at this point, we may not even think of the bias’s effects.

Regulations have
been placed on funding bias, note the “Sponsored” tag and other
evidence for example on social media platforms when influencers post with
“#ad.” These indicators are set up to clue in the consumer that their
purchasing decisions are being biased by funded posts, something relatively new
the last several years. Corporations spent years biasing search history towards
paid posts without having to let the consumer know, this is a huge stride
against hidden data bias. There’s nothing wrong with “pay to play!”
As a consumer, this can be a helpful way to find one of your new favorite
brands, products or technology solutions but people now believe it’s important
for the consumer to know when funding is biasing their purchase.

Another example of
bias in this search are the items marked with the “best seller” tag.
Under “Help & Customer Service” Amazon.com says: “The Amazon
Best Sellers calculation is based on Amazon.com sales and is updated hourly to
reflect recent and historical sales of every item sold on Amazon.com.”
There is a litany of data bias that could be going on here. Was the
“Simple Houseware” organizer at one point a “sponsored”
post which led to it becoming a “best seller?” A quick google search
shows that Simple Houseware only sells on Amazon, this could be another
contributing factor to the status as a “best seller.” We simply do
not know the parameters the calculation is setting for “sales” as
that is quite a broad term.

To play devil’s
advocate, the “sponsored” 
results could be amazing products that you will purchase, love and even
re-order in the future. The data bias written into the “Best Sellers”
calculation could absolutely be favoring great products you will know and love.
However, it’s critical that we don’t turn a blind eye to the bias and we
continue to scroll past the “sponsored” and “best sellers”
to get a full picture for purchasing.

The objective of
this article isn’t to get you to stop buying from Amazon but rather to consider
the data bias right in front of you. If there is this much data bias in one
simple Amazon search, how many more things in your life are impacted by data
bias? How much is your business and its bottom line impacted by data bias?
Algorithms, AI and their inherent bias are a part of daily life. How will you
use them to create change for good?

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

By clicking subscribe, you agree to share your email address with the site owner to receive post updates from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.

Posted on

Data Bias on the Daily

Our upcoming September Blog series is Data Bias on the Daily: How Data Bias in Artificial Intelligence is Impacting You. This series will focus on data bias in various forms encountered daily and the goal is to educate consumers on how bias can enter algorithms, knowingly and unknowingly. It is important to define bias:

Bias: The systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results.

How to Identify Statistical Bias

Bias in Artificial Intelligence and algorithms is sometimes intentional, can be caused by a number of things including, but not limited to, sample selection or data collection, and can be avoidable, if that is the desired outcome. (Many corporations want to write their algorithms with bias, in order to increase their bottom line.)

Maybe you’re a woman searching for a STEM job, but the data is biased against your application? Perhaps, your Amazon search is biased towards products and brands that will only increase Amazon’s bottom line? Data bias is even entering our judicial system, is it possible that algorithmic “advancement” is simply confirming long standing racial bias?

Stay tuned to learn more about how Data Bias impacts our daily lives by checking in with us on Mondays in September (9/16, 9/23 and 9/30).

By clicking submit, you agree to share your email address with the site owner to receive marketing, updates, and other emails from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Processing…
Success! You're on the list.