Will computers be allowed to do more than "aping"

Yesterday I completed reading, "What Can Bonobos Teach Us About The Nature of Language," in the July-August 2020 edition of the Smithsonian Magazine. Lindsay Stern (Twitter, LinkedIn) wrote the article, which is about the Bonobo ape and their ability to grasp complex language concepts using lexigrams. The research was pioneered by Dr. Sue Savage-Rumbaugh (Linkedin). Sue was able to communicate with lexigrams to the Bonobos apes. She would show a banana and point to a specific symbol. Over time, the bonobos mapped the symbol for bananas, apples, and other objects and communicated with Sue and other researchers. Dr. Savage-Rumbaugh was eventually able to communicate complex thoughts using the lexigrams. Dr. Savage-Rumbaugh was eventually replaced due to the close bond she developed with the bonobos. The current researches state that Dr. Savage-Rumbaugh deviated from the scientific rigors in her research and, in many ways, neglected the apes.

In her Smithsonian article, Stern questions if humans don't want to develop a relationship with the bonobos because it makes us less human or the apes more human. The thought is intriguing, and it led me to ponder if humanity will a similar struggle with machines in the near or distant future. After all, we teach computers using deep-learning algorithms to map images, which can be equated to lexigrams, to objects. Unlike animals, machines are predictable and can be harnessed to superhuman tasks like crunching through petabytes of data in a matter of minutes. If a PC or a Mac says, "I don't want to do the task, " then we replace the machinery. If an employee says, " I don't want to do the task," then the employee is disciplined or even fired. Is it a control thing; or is it our biases (acquired or built-in)

As a trained data scientist, I was taught that it is tough to remove the audience's biases. The best a data scientist can do is to provide the data in a consumable format that will enable the audience to get insights and make decisions.   

Organizations and governments fund research to learn about things that can be leveraged to benefit their respective groups and, eventually, humanity. However, these entities will not fund research that questions the underlying belief systems and makes us less human or our research subject or technology more human. Hopefully, you enjoyed the blog entry because the Smithsonian article made me reflect on the work we do and how we contribute and transform the "human race."

Hack the LIME - the story about open-source AI

Leave it to a lawyer to spoil the fun!  Andrew Burt, a lawyer, and writer, recently wrote an article for the Harvard Business Review, which discusses the risks of open-source Artifical Intelligence (AI) models   The HBR post, "The AI Transparency Paradox," explains the dangers of making artificial intelligence models transparent.  On the one hand, businesses want to know how companies engineered their AI algorithms.  Is the AI algorithm too sexist, racist, and just not good?  For instance, in James Vincent's article, Google ‘fixed’ its racist algorithm by removing gorillas from its image-labeling tech, Google "fixed" its 2015 deep learning algorithm, which labeled humans as monkeys. I am sure the appropriate stakeholders at Google wanted to know how the algorithm was written and how can the developer fixed the inappropriate bias in the algorithm.

On the one hand, if Google opened its AI code to the open-source community, then the AI algorithms could be significantly improved.   On the other hand, as (our no-fun lawyer friend), Andrew Burt cautions businesses that they would be vulnerable if they opened their AI algorithms to the world, which includes bad actors like hostile governments.  Andrew uses the research paper titled, “Why Should I Trust You?” Explaining the Predictions of Any Classifier.  The paper is about the Local Interpretable Model-Agnostic Explanations (LIME) algorithm.  Andrew then talks about a research paper. How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods,  which was published in November 2019.  The document discusses a "novel scaffolding technique," which can be used to hack the LIME algorithm.  Is there a return on investment if companies made their algorithms more transparent?

Folks may argue that Google successfully made its TensorFlow machine learning (ML) platform open source without much impact.  Before the TensorFlow platform question can be answered,  the following terms need to be defined:

• Machine Learning;
• Deep Learning; and 
• Artificial Intelligence.

According to Geeks for Geeks portal, "Machine Learning is a technique of parsing data, learn from that data, and then apply what they have learned to make an informed decision." Machine Learning includes supervised training where the machine builds the model on most of the data (70% to 90% of the complete dataset) and then test the data for accuracy with the remaining portion of the data.  Unsupervised training involves machine learning from the full dataset.  There is no testing of the data.

Deep Learning algorithms are a specific subset of Machine Learning algorithms, which are primarily composed of neural networks. A common use of deep learning algorithms is for image recognization.

Gartner defines Artificial intelligence (AI) is the application of "...advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions."

Gartner recommends several open-source Data Science and ML platforms, including TensorFlow, to develop ML-based solutions.    Does this mean Mr. Burt is incorrect?

Andrew Burt is correct because comprised algorithms, which are the core of all AI solutions, will cause businesses to make poor decisions and ultimately discredit the AI solution. The underlying technology, which enables to invoke the algorithm, pull the data, process the data, and reports the outcomes, can be open source.  The other critical piece of any AI solution is the data itself.   Sensitive data is never unless asked by law enforcement or regulatory agency.

In conclusion,  Andrew Burt is correct by bringing up valid points, but at the same time, the AI field is still pretty young.  I am a strong believer in open-source software, and I believe we will have open-source AI models available.  We will also have AI models that will validate other models using technologies like blockchain technologies.

NOTE: The video below is about the LIME algorithm, and please keep in mind researchers recently hacked this algorithm.