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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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.

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?


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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).



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