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Tag: Machine Learning

Domain and LLM

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I am in total agreement with Morgan Zimmerman, Dassault Systems quote in TOI today.  Every industry has its own terminologies, concepts, names, words i.e Industry Language. He says even a simple looking word like “Certification” have different meanings in Aerospace vs life sciences.  He recommends use of Industry specific language and your own company specific language for getting significant benefit out of LLMs. This will also reduce hallucinations and misunderstanding.

This is in line with @AiThoughts.Org thoughts on Domain and company specific information on top of general data used by all LLMs.  Like they say in Real Estate, the 3 most important things in any real estate buy decision is “Location, Location and Location”.  We need 3 things to make LLMs work for the enterprise.  “Domain, Domain and Domain”.   Many of us may recall a very successful Bill Clinton Presidential campaign slogan. “The economy, Stupid”.   We can say “The domain, Stupid” as the slogan to make LLMs useful for the enterprises.

But the million-dollar question is how much it is going to cost for the learning updates using your Domain and company data?  EY published a cost of $1.4 Billion which very few can afford.  We need much less expensive solutions for large scale implementation of LLMs.

Solicit your thoughts. #LLM #aiml #Aiethics #Aiforindustry

L Ravichandran

AI Legislation: Need Urgency

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Let me first wish you all a happy Navratri Festivities.  I still fondly remember the Durga Pooja days during my Indian Statistical Institute years.  However, we also need to remember we are in the midst of two wars, one in Ukraine and other in Middle East.  We wish solutions are found and further loss of life and destruction is stopped.

I came across two articles in Hindu Newspaper regarding our topic AI.  I have attached a scan of an editorial by M.K. Narayanan, well known National Security and Cyber expert. 

Few highlights are worth mentioning for all of us to ponder.

  1. There is a general agreement that latest advances in AI do pose a major threat and need to be regulated like nuclear power technologies.
  • All countries are not only “locking the gates after the horse has bolted””, but “discussing about locking the gates and deciding on the make & model of the Lock while the horse has bolted”.  Huge delays in enacting and implementing AI Legislation is flagged as a big issue.
  • Rogue nations who willfully decide not to enforce any regulations will get huge advantage over law abiding nations.
  • More than 50% of the large enterprises are sitting on “intangible” assets which are at huge risk of evaporating by non-state actors with AI powered cyber warfare.
  • Cognitive warfare using AI technologies, will destabilize governments, news media and alter the human cognition.
  • This is a new kind of war fare where state and technology companies must closely collaborate.
  • Another interesting mention of over dependence on AI and algorithms which may have caused the major intelligence failure in the latest middle east conflict.

All of these point to the same conclusion.  All countries and multi-lateral organizations such as UN, EU, African Union, G20 etc., multi-lateral military alliances like NATO etc. must move at lightning speed to understand and agree on measures to effectively control and use this great technology.  

The old classic advertisement slogan “JUST DO IT” must be the motto of all the organizations.

Similar efforts are needed by all large enterprises, large financial institutions, regulatory agencies to get ready for the scale implementation of these technologies.

Last but not the least, large technology companies need to look at this not just as a form of another innovation to help automation, but a human affecting , major disruption causing technology and spend sufficient resources in understanding and putting sufficient brakes to avoid run away type situations.

Cyber Security, Ethical auditors, risk management auditors will have huge opportunities and they have to start upskilling fast.

More later,

L Ravichandran.

AI Regulations : Need for urgency

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Few weeks ago, I saw a news article about risks of unregulated AI.  The news article quoted that in USA, Police came to a house of a 8 months pregnant African American lady and arrested her due to a facial recognition system identified her as the theft suspect in a robbery. No amount of pleading from the lady about her advanced pregnancy condition during the time of robbery and she just could not have committed the said crime with this condition, was heard by the police officer.  The Police officer did not have any discretion.  The system set up was such that once the AI face recognition identifies the suspect, Police are required to arrest her, bring her to the police station and book her.  

In this case, she was taken to the police station, booked and released on bail. Few days later the case against her was dismissed as the AI system has wrongly identified her.  It was also found out that she was not the first case and few more people, especially African American women were wrongly arrested and released later due to incorrect facial recognition model.

The speed in which the governments are moving on regulations and proliferation of AI tech companies delivering business application such as this facial recognition model demand urgent regulations.

May be citizens themselves should organize and let the people responsible for deploying these systems accountable.  The Chief of Police, may be the Mayor of the town and County officials who signed off this AI facial recognition system, should be made accountable.  May be the County should pay hefty fines and just not a simple oops, sorry.

Lots of attention need to be placed on training data.  Training data should represent all the diverse people in the country in sufficient samples.  Expected biases due to lack of sufficient diversity in training data must be anticipated and the model tweaked.  Most democratic countries have criminal justice system with a unwritten motto “Let 1000 criminals go free but not a single innocent person should go to jail”.  The burden of proof of guilt is always on the state.  However, we seem to have forgotten this when deploying these law enforcement systems.  The burden of proof with very high confidence levels and explainable AI human understandable reasoning, must be the basic approval criteria for these systems to be deployed.

The proposed EU act classifies these law enforcement systems as high risk and will be under the act.  Hopefully the EU act becomes a law soon and avoid this unfortunate violation of civil liberty and human rights.

More Later,

L Ravichandran

AI for Sustainability and Sustainability in AI

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I will be referring to the following 3 papers on this very interesting topic.

(1}  https://link.springer.com/article/10.1007/s43681-021-00043-6

 Sustainable AI: AI for sustainability and the sustainability of AI

A Van Wynsberghe – AI and Ethics, 2021 – Springe

(2) https://www.researchgate.net/publication/342763375_Carbontracker_Tracking_and_Predicting_the_Carbon_Footprint_of_Training_Deep_Learning_Models/link/5f0ef0f2a6fdcc3ed7083852/download

(3)      Lacoste, A., Luccioni, A., Schmidt, V., Dandres T.: Quantifying

the Carbon Emissions of Machine Learning. (2019)

While there is a tremendous push for using new-generation generative AI based on large language models to solve business applications, there are also voices of concern from experts in the community about the dangers and ethical consequences.  A lot has been written about this but one aspect which has not picked up sufficient traction, in my opinion, is Sustainable AI.  

In (1), Wynsberghe defines two disciplines on AI & sustainability.   AI for Sustainability and Sustainable AI.

AI for Sustainability is any business application using AIML technology to solve climate problems.  Use of this new generation technology to help in climate change and CO2 reductions.   Major applications are getting developed for optimal energy distribution across renewable and fossil energy sources. Any % extra use from renewable sources, help in less use of fossil fuels and help in climate change.  Various other applications may include better climate predictions and the use of less water, pesticides, and fertilizers for food production.  Many Industry 4.0 applications to build new smart factories, smart cities, and smart buildings fall into this category.

On the other hand, Sustainable AI measures the massive use of GPU and other computing, storage, and communications energy usage while building the AI models and suggest ways to reduce this.  While digital software development and testing can be done in a few developers’ laptops with minimal use of IT resources, the AIML software development life cycle calls for the use of massive training data and develop deep learning neural networks with multiple millions of nodes.   Some of the new generation Large Language models use billions of parameters beyond the imagination of all of us.  The energy use does not stop here.  Fine Tuning learning for specific domains or relearning is as energy-consuming or sometimes even more than original Training.   Some numbers mentioned in (1) are reproduced here to highlight the point.   One deep-learning NLP model consumed energy equivalent to 600,000 lbs of CO2.  Google Alpha-Go-Zero generated over 90 Tonnes of CO2 over 40 days it took for the initial training.  These numbers are large and at least call for review and discussions.   I hope I have been able to open your eyes and generate some interest in this new dimension of AI & Ethics i.e impact on climate change.

I am sure many of you will ask “Isn’t any next-generation industrialization from horse carriages to automobiles or steam engines to combustion always increased the use of energy and why do we need to worry about this for AI?”.  Or “There has been so much talk on how many light bulbs one can light for the same power used for a simple google search , why worry about this now ?”.  All valid questions.  

However, I will argue that

  1. The current climate change situation is already in a critical stage and any unplanned large-scale usage new of energy can become “the feather that broke the camel’s back!”.
  2. Use of fully data driven life cycle and billions of parameters, deep neural networks are being used for the first time at an industrial scale and industry-wide and there are too many unknowns.

What are the suggestions?

  • Energy consumption measurement and publication must become part of the AI & Ethics practice followed by all AI development organizations.   (2)  Carbon Tracker Tool and (3) Machine learning emission calculator are suggestions for this crucial measurement.  I strongly recommend organizations use their Quality & Metrics departments to research and agree on a measurement acceptable to all within each organization.  More research and discussions need to calculate the net increased use of energy compared to current IT tools to get the right measurement. In some cases, the current IT tools may be using legacy mainframes and expensive dedicated communication lines using up large amounts of energy and the net difference by using AIML may not be that large.
  • Predicting the energy use at the beginning of the AIML project life cycle also is required. (3). 
  • The prediction data of CO2 equivalent emissions need to be used as another cost in approving AIML projects.
  • Emission prediction also will force AIML developers to select the right size training data and use of right models for the application. Avoid the temptation of running the model on billions of data sets just because data is available!. Use the right tools for the right job.  You don’t need a tank to kill an ant!.
  • Ask the question of whether the use of deep learning is appropriate for this business application? For example, a simple HR application used for recruitment or employee loyalty prediction with Deep learning models may turn out to be too expensive in terms of Co2 emissions and need not be considered a viable project.
  • CEOs include this data in their Climate Change Initiatives Report to the Board and shareholders and also deduct carbon credits used up by these AIML applications in the company’s Carbon credit commitments.

More Later,

L Ravichandran