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Beyond the script: The future of digital customer service

Posted on July 24, 2024 - 10:20 am by

In the past companies noticed that their customers are getting frustrated by waiting for customer service agents for simple queries. “All our agents are busy. Your call is important to us. Please wait.” became a dreaded message for customers looking for answer to their simple queries. So many companies launched Chat Bots as part digitalizing their customer service.

Chat Bots could address only one set of customers – those who had very basic queries. Chatbots are programmed to respond to specific commands or questions with predefined responses. Can’t grasp complex questions or deviate from their script. Offers generic answers based on programmed responses – Relies on pre-programmed rules and keywords. Doesn’t improve over time and requires manual updates.

Then came AI Chat Bots with a promise to divert more customers away from human agents. They are expected to be smarter and more Flexible due to utilization of Natural Language Processing (NLP) to understand intent and context. Can handle a wider range of questions and respond in a more natural way. Adapts and improves through machine learning, offering more relevant responses over time. Can tailor responses based on user history and preferences. These Chat Bots were expected to reduce response times and improving the overall customer experience.

So, what is the customer behavior after this advancement in Chat Bot technology?  A recent survey commissioned by customer experience platform CallVu throws some interesting light on it.

Figure 1: Source: CallVu – AI in Customer Service Survey Mar 2024

A significant percentage of people, 81%, indicated readiness to wait, for varying duration, to talk with a live agent. 16% people were ready to wait for more than 10 min to talk to live agents! Only 14% seems to be ready to go straight away for interacting with Chat Bots.

Now combine the above survey findings with the findings below, in the same report.

Figure 2 Source: CallVu – AI in Customer Service Survey Mar 2024

As CallVu found out, people rated live agents much higher than AI Assistants on most dimensions. Slight rating advantages for AI assistants on speed, patience and accuracy.

The interesting part to note is customers prefer talking to live agents for venting out frustration, indicating a role beyond just problem resolution – exhibition of empathy. However, it is again clear that customers prefer interacting with Chat Bots for simple queries with accurate answers. The Chat Bot interaction also seems to give a feeling of ‘being patient’ to the customers.

Does this mean there is no road ahead for digitalization of Customer Services interactions using Chat Bots? Few other surveys do show data to the contrary. The sixth edition of Salesforce’s ‘State of Connected Customer’ report clearly brings out the fact that 61% of the customers still would prefer to engage a self-service to resolve an issue. But there is a warning from 68% people that if there is a bad experience then they will never use self-service again for that company. With these findings, Salesforce makes a case for an opportunity to further improve the experience through a more intelligent, autonomous agents powered by Generative AI.

If we look at what Salesforce promises through its ‘Einstein’ Autonomous AI Service Agent, it gives a peek into what to expect from such agents when other Independent Software Vendors start delivering similar products into the market.

Sophisticated reasoning and natural responses: Fluid, intelligent conversations; coupled with logical inferences and connecting various pieces of information from company’s various data sources.

24/7 swift resolutions driven by trusted data: Grounded in company’s trusted business data.

Built-in guardrails: Including protection of PII (Personally Identifiable Information).

Cross-channel and multimodal: Self-service portals, WhatsApp, Apple Messages for Business, Facebook Messenger, SMS and so on.

Seamless handoffs to human agents: Seamless handoff to Human Agent, if needed, with full context of conversation. For example, if something needs to be handled outside defined policy.

Only time will tell whether this will move the needle in the right direction for customers to start relying on digital means more and more to get their service requests resolved. In the near future, we might see a hybrid environment where all three types coexist. Traditional chatbots can handle simple tasks, while AI chatbots manage complex interactions. Autonomous AI chatbots can take on more advanced roles, working alongside humans.

AI use cases for the Manufacturing Vertical

Posted on June 6, 2024 - 5:00 am by

AI Use cases in Industrial Vertical

Lots of news and talk about AI Usecases  in Technology, retail consumer and health care verticals.  I feel we need more focus on manufacturing vertical which can get enormous benefits from this exciting new technology. 

After large amount of shifting manufacturing to low cost countries, now rich/developed countries are moving back factories back in to their home country due to voter’s concern on job losses.  However, the same voters are not willing to pay higher prices for their goods and services.  Hence the focus is on use of AI technologies to the fullest to manage the jobs and cost conundrum.

Andrew NG, AI Guru, who is focusing on effective deployment of this new technology in enterprises.  They are promoting Domain Specific Visual Models targeting at manufacturing, electronics, food & beverages verticals. 

We at @AiThoughts.Org agree with Landing.Ai approach and started advocating use of Vision solutions to solve many problems in the manufacturing vertical.  We debated with our experts on manufacturing and identified a sample e of few usecases worth consideration.

  1. Quality control of incoming supplier materials at the receiving dock or even at shipping dock at the supplier’s premises.  With the complete eco system in the manufacturing life cycle at extreme margin pressures, short cutting of quality of the materials supplied is a real concern and any fast , mostly automated solution will be a great solution.  Bad quality material will eventually the manufacturers end product bad and increase customer returns or customer complaints. In many cases, this may even disrupt the assembly line process causing unplanned shut downs costing $$s.
  2. Quality control of sub-assemblies and final product.  Landing.Ai’s first product launch for based on this use case.   Customer returns or customer service requests post shipment are huge concerns of any manufacturing company.  Any solution where large amount of sub-assembly and end products can be quality checked by AI models will be very useful.  In many cases, this may not be a human replacement solution but human enhanced solution.
  3. With factories becoming more automated with conveyor belts, and human-Robot work flow assemblies, employee safety in factory premises becomes a major concern.  AI visual solutions to ensure humans are safe at all times, provide ample warnings to humans to stay away from risk areas and even shut down systems such as conveyor belts, robot assembly etc. to save a human worker from harm are needed.
  4. I can go on with other solutions as I am very passionate about Manufacturing and spent first 15 years of my IT career helping customers in the Manufacturing verticals.

As a bonus, there are problems in the global supply chain which we still need to be solved using AI.  The optimization of JIT vs safe inventory both for raw  materials and finished products is a long standing problem.  Even large enterprise software providers have not found any new solutions or new algorithms to attack this problem. The industry needs a holistic AI based solution taking in to consideration the end to end life cycle from supplier production, global shipping, port loading/unloading work flows, road transportation, quality rejections, own factory scheduled maintenance , customer orders and host of other employee union/strike related issues at supplier side and own side …  The problem is very complex and needs a good AI solution.

 

Hoping that this post will create more interest in the manufacturing vertical amongst the T community and some solutions will come along.  Ai.Thoughts@org is available for any help in this regard.

More later.

L Ravichandran

AI Symphony in Airline Enterprises

Posted on May 5, 2024 - 12:35 pm by

Good old-fashioned AI (or what is now called Traditional AI) is deterministic in nature, while Generative AI is more probabilistic. Traditional AI relies on explicit rules, logic, and predefined algorithms. Given the same input and conditions, it will always produce the same output. This predictability ensures transparent behavior and decision-making processes.

Generative AI, on the other hand, generates new content after learning from data, expressing outcomes as probabilities. It adapts to different contexts and produces varied outputs even with the same input.

By combining Traditional AI for stability, interpretable decision-making, and well-defined rules in critical tasks, with Generative AI for creativity, adaptability, and handling complex, unstructured data, enterprises can create powerful systems that balance reliability and innovation.

This blog explores how this AI Symphony can be used in the context of an Airline Crew Scheduling System.

The Airline industry is a dynamic sector, where every aspect of its operations demands meticulous planning and execution. One of the core components of these operations is the task of the Crew Scheduling, which has to accommodate a wide range of variables and unforeseen events. The purpose of Crew Scheduling is to define where crew members will be on set dates and times. At the heart of crew scheduling is the Crew Roster. The diagram below brings out some of the key dimensions, if not all, that impact the activity of arriving at an optimal crew schedule. The diagram also outlines some of the key variables that are part of each of the impacting dimensions. For some of the terminologies involved, you can check out Ref 1.

 

 

The key dimensions that drive the schedule are:
Business related – Covers variables such as aircrafts, routes, schedules.
Crew related – Covers variables such as availability, roster bids, skills, training schedules, medical checks, license validity.
Disruptions – Covers events like technical failures, weather conditions, crew emergencies.
Legal requirements – Covers constraints like flight time, duty time, minimum rest period.
Labour union agreements – Covers constraints like agreed work hours, scheduling rules, pay and compensation, seniority considerations in roster bidding.
Crew Pairing – Covers requirements like flight pairing to be fulfilled by pairing crews. A flight pairing (also known as a trip or crew rotation) is a sequence of nonstop flights (flight legs) that starts and ends at the same airport. Once flight pairings are established, airlines can then assign crew members (crew pairing) specific tasks based on these designated flights.
Suppose an airline operates flights from New York (JFK) to London (LHR) and back. A pairing could be:
JFK-LHR (outbound leg)
LHR-JFK (return leg)
The crew assigned to this pairing would fly from JFK to LHR, rest, and then fly back to JFK.
Deadhead travels – A deadhead refers to a flight within a trip sequence where a crew member (such as a pilot or flight attendant) is not scheduled to work. Deadheads are necessary when trip continuity fails due to delays or cancellations and crew is required to reach a location to take over the shift. It’s a cost to the airline as a ticket revenue is lost.
Its planning has to accommodate available Deadhead routes, cost of travel on that route, conflict of crew rest period requirements with duration of travel on the route.

The key outcomes of scheduling exercise are:
Crew Roster – Shows assigned duties and responsibilities of each crew member. It ensures that the correct number of crew members are scheduled work at all times and to ensure that they are properly rested.
Crew communications and Notifications – Through SMS, WhatsApp or automated voice calls. The duty assignments, schedule changes, flight update etc. are conveyed to crew.

The future
Digitalization of Crew Scheduling and Roster Management has happened through IT Systems. Some levels of mathematical rules are incorporated in these systems to help the planners carry out the job. Combination of Traditional AI and Generative AI has potential to take this digitalization further to bring down the people intensity involved in creating these digital rosters making it more responsive to unknown events.

In general, Traditional AI is better for rule-based, deterministic tasks, while Generative AI excels in creative content generation and learning from data. Some of the areas covered below will provide appreciation of how these two AI types can work together. Though this is not an exhaustive list of possibilities.

  • Crew Assignment Optimization: Crew assignment optimization involves creating efficient schedules for crew members based on predefined rules, multiple variables  and constraints. Traditional AI can handle this well, as it relies on deterministic algorithms to find optimal assignments.
  • Rostering: Automating the rostering process where all the required impacting elements, except may be disruptions, are accommodated is handled well by using Traditional AI. If the airline wants to implement Dynamic Rostering, which takes care of the learning from historical data and adjusting schedules based on changing conditions (e.g., flight delays, crew last minute demands), then Generative AI is more suitable. Generative AI is able to propose options that were never thought of before to make the roster more dynamic.
  • Pairing Optimization: Pairing optimization involves creating efficient sequences of flights (pairings) for crew members. Such requirement is well suited for Traditional AI due to its deterministic behaviour.
  • Deadhead crew positioning: In the context of deadhead positioning, having clear rules and protocols is crucial for efficient repositioning. Deadhead positioning requires immediate decisions based on operational needs (e.g., flight delays, crew availability) with stability and predictability. Adherence to legal regulations (e.g., duty time limits, rest requirements) is critical during disruptions. No creativity is acceptable here. It’s a large-scale repositioning exercise that needs to be managed timely with scalability. Hence Traditional AI is suitable here.
  • Crew Communications and Notifications: Traditional AI can handle automated crew notifications (e.g., flight changes, duty reminders) based on predefined triggers. So suitable for routine communications. The interaction with the crews can be further enhanced by bringing in Generative AI Chatbots using natural language interactions for crew queries, assist with logistics, roster bids, crew swaps possibilities and so on. The interaction can become more context sensitive, for example in logistics assistance, based on crew’s current location, current weather condition there, current traffic situation there and so on. This also reduces administrative workload.

The above are the traditional scheduling reas. Some other possible areas where Generative AI can complement Traditional AI are as below.

  • Scenario Exploration and Contingency Planning: Generative AI can simulate alternative scheduling scenarios based on historical patterns. It can explore “what-if” situations, such as crew shortages, equipment failures, or unexpected events. By creatively generating various scenarios, it helps airlines prepare for contingencies.
  • Predictive Crew Sickness and Fatigue Management: Generative AI can analyze crew health data, historical sickness patterns, and fatigue indicators. It can predict potential crew shortages due to sickness or fatigue. Creativity lies in identifying early warning signs and suggesting preventive measures.

This use case brings out the fact that most of the operational ‘AIfication’ use cases in enterprises will be a hybrid approach involving Traditional AI and Generative AI.

 

References:

  1. Understanding Cabin Crew Roster https://cabincrewhq.com/cabin-crew-roster/

 

Note: The diagram was created using “Xmind” mind mapping tool from XMIND LTD.

Insights into AI Landscape – A Preface

Posted on November 1, 2023 - 4:49 pm by

AI Landscape and Key Areas of Interest

The AI landscape encompasses several crucial domains, and it’s imperative for any organization aiming to participate in this transformative movement to grasp these aspects. Our objective is to offer our insights and perspective into each of these critical domains through a series of articles on this platform.

We will explore key topics each area depicted in the diagram below.

1.      Standards, Framework, Assurance: We will address the upcoming International Standards and Frameworks, as well as those currently in effect. Significant efforts in this area are being undertaken by international organizations like ISO, IEEE, BSI, DIN, and others to establish order by defining these standards. This also encompasses Assurance frameworks, Ethics frameworks, and the necessary checks and balances for the development of AI solutions. It’s important to note that many of these frameworks are still in development and are being complemented by Regulations and Laws. Certain frameworks related to Cybersecurity and Privacy Regulations (e.g., GDPR) are expected to become de facto reference points. More details will be provided in the forthcoming comprehensive write-up in Series 1.

2.      Legislations, Laws, Regulations: Virtually all countries have recognized the implications and impact of AI on both professional and personal behavior, prompting many to work on establishing fundamental but essential legislations to safeguard human interests. This initiative began a couple of years ago and has gained significant momentum, especially with the introduction of Generative AI tools and platforms. Europe is taking the lead in implementing legislation ahead of many other nations, and countries like the USA, Canada, China, India, and others are also actively engaged in this area. We will delve deeper into this topic in Series 2.

3.      AI Platforms & Tools: AI Platforms and Tools: An array of AI platforms and tools is available, spanning various domains, including Content Creation, Software Development, Language Translation, Healthcare, Finance, Gaming, Design/Arts, and more. Generative AI tools encompass applications such as ChatGpt, Copilot, Dall-E2, Scribe, Jasper, etc. Additionally, AI chatbots like Chatgpt, Google Bard, Microsoft AI Bing, Jasper Chat, and ChatSpot, among others, are part of this landscape. This section will provide insights into key platforms and tools, including open-source options that cater to the needs of users.

4.      Social Impact:  AI Ethics begins at the strategic planning and design of AI systems. Various frameworks are currently under discussion due to their far-reaching societal consequences, leading to extensive debates on this subject. Furthermore, it has a significant influence on the jobs of the future, particularly in terms of regional outcomes, the types of jobs that will emerge, and those that will be enhanced or automated. The frameworks, standards, and legislations mentioned earlier strongly emphasize this dimension and are under close scrutiny. Most importantly, it is intriguing to observe the global adoption of AI solutions and whether societies worldwide embrace them or remain cautious. This section aims to shed light on this perspective.

5.      Others: Use Cases and Considerations:  In this Section, we will explore several use cases and success stories of AI implementation across various domains. We will also highlight obstacles in the adoption of AI, encompassing factors such as the pace of adoption, the integration of AI with existing legacy systems, and the trade-offs between new solutions and their associated costs and benefits.  We have already published a recent paper on this subject, and we plan to share more insights as the series continues to unfold.

The Executive Order!

Posted on November 1, 2023 - 6:20 am by

Close on the heels of the formation of the Frontier Model Forum and a White House announcement that it had secured “voluntary commitments” from seven leading A.I companies to self-regulate the risks posed by artificial intelligence, President Joe Biden, yesterday issued an executive order regulating the development and ensuring safe and secure deployment of artificial intelligence models . The underlying principles of the order can be summarized in the picture.

The key aspects of the order focus on what is termed “dual-use foundation models” – models that are trained on broad data, uses self-supervision, and can be applied in a variety of contexts. Typically the generative AI models like GPT fall into this category, although, the order is aimed at the next generation of models beyond GPT-4.

Let’s look at what are the key aspects of what the order says in this part. Whilst the order talks about the

Safe & Secure AI

  • The need for safe and secure AI through thorough testing – even sharing test results with the government for critical systems that can impact national security, economy, public health and safety
  • Build guidelines to conduct AI red-teaming tests that involves assessing and managing the safety, security, and trustworthiness of AI models
  • The need to establish provenance of AI generated content
  • Ensure that compute & data are not in the hands of few colluding companies and ensuring that new businesses can thrive [This is probably the biggest “I don’t trust you” statement back to Big Tech!]

AI Education / Upskilling

  • Given its criticality, the need for investments in AI related education, training, R&D and protection of IP.
  • Support for programs to provide Americans with the skills they need for the age of AI and attract the world’s AI talent, via investments in AI-related education, training, development, research, and capacity and IP development
  • Encouraging AI skills import into the US [probably the one that most Indian STEM students who hope to study and work in the US will find a reason to cheer]

Protection Of Rights

  • Ensuring the protection of civil rights, protection against bias & discrimination, rights of consumers (users)
  • Lastly, also the growth of governmental capacity to regulate, govern and support for responsible AI.

Development of guidelines & standards

  • Building up on the Blueprint AI Bill of Rights & the AI Risk Management Framework, to create guidance and benchmarks for evaluating and auditing AI capabilities, particularly in areas where AI could cause harm, such as cybersecurity and biosecurity

Protecting US Interests

  • The regulations also propose that companies developing or intending to develop potential dual-use foundation models to report to the Govt on an ongoing basis their activities w.r.t training & assurance on the models and the the results of any red-team testing conducted
  • IaaS providers report on the security of their infrastructure and the usage of compute (large enough to train these dual use foundation models), as well as its usage by foreign actors who train large AI models which could be used for malafide purposes

Securing Critical Infrastructure

  • With respect to critical infrastructure, the order directs that under the Secretary Homeland Security, an AI Safety & Security Board is established, composed of AI experts from various sectors, to provide advice and recommendations to improve security, resilience, and incident response related to AI usage in critical infrastructure
  • All critical infrastructure is assessed for potential risks (vulnerabilities to critical failures, physical attacks, and cyberattacks) associated with the use of AI in critical infrastructure.
  • An assessment to be undertaken of the risks of AI misuse in developing threats in key areas like CBRN (chemical, biological, radiological and nuclear) & bio sciences

Data Privacy

  • One section of the document deals with mitigating privacy risks associated with AI, including an assessment and standards on the collection and use of information about individuals.
  • It also wants to ensure that the collection, use, and retention of data ensures that privacy and confidentiality are respected
  • Also calls for Congress to pass Data Privacy legislation

Federal Government Use of AI

  • The order encourages the use of AI, particularly generative AI, with safeguards in place and appropriate training, across federal agencies, except for national security systems.
  • It also calls for an interagency council to be established to coordinate AI development and use.

Finally, the key element – keeping America’s leadership in AI strong – by driving efforts to expand engagements with international allies and establish international frameworks for managing AI risks and benefits as well as driving an AI research agenda.

In subsequent posts, we will look at reactions, and what it means for Big Tech and for the Indian IT industry which is heavily tied to the US!

Domain and LLM

Posted on October 27, 2023 - 4:45 am by

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

Posted on October 20, 2023 - 4:50 am by

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 and Law

Posted on September 23, 2023 - 3:28 pm by

The Public Domain is full of initiatives by many Law Universities, large law firms, and various government departments on the topic of “AI and Law “. I was happy to see a news article a few days ago about the Indian Consumer grievances cell thinking about using AI to clear a large number of pending cases. They have had some success in streamlining processes and making it all digital but they felt that the sheer large volume of pending cases needs AI-type intervention.  I have already talked about the huge volume of civil cases pending in lower courts in India and some cases taking even 20 years to get final judgment.  As the saying goes “Justice delayed is Justice denied”, it is imperative that we find solutions to this huge backlog problem.

All discussions are centred around two broad areas: –

1.      Legal Research and development of customer’s case by Law firms.  Basically, core work of both junior and senior law associates and partners.

2.      Assisting judges or even rendering judgment on their own by AI models to reduce backlog and speedy justice. 

Lots of interesting discussions happening on (1). Law research, looking into archives, similar judgments, precedence’s, etc. seem to be a no-brainer.  Huge advances in automation have been already done and this will increase multi-fold by these Law purpose-built language models.  What will happen to junior law associates is an interesting question. Can they use better research and develop actual arguments and superior case brief for their clients and take the load off senior associates who in turn can focus more on customer interactions?  I found discussions on the model analysing judges’ earlier judgments and making the argument briefs customized per judge, fascinating.  

The no (2) item needs lot of discussions.   All democratic countries jurisprudence is based on these 3 fundamental principles.

  1. Every citizen will have their “day in the court” to present their case to an impartial judge.
  2. Every citizen will have a right to a competent counsel with a provision of public defenders given free to the citizens.
  3. Every witness can be cross examined by the other party without any restrictions.

On the one hand, we have these great jurisprudence principles.  On the other hand, we have huge backlogs and delays. 

How much citizens are willing to give up some of the basic principles to get speedy justice? 

Can we give up the principle of “my day in Court” and let only written briefs submitted to the court to be used for final judgement? This will mean witness statements in briefs will not be cross examined or questioned.

Can we give up the presence of a human judge who will read the briefs on both sides and make a judgement and let an AI Model read both the briefs and pronounce the judgement?

Even if citizens are willing to give up these principles, does the existing law of the land allow this?   It may require changes to law and in some countries even changes to their constitution to allow for this new AI jurisprudence.

Do we differentiate between civil cases and criminal cases separately and find different solutions?  Criminal cases will involve human liberty issues such as imprisonment and will need a whole set of different benchmarks.

What about changes to appeal process if you do not like lower court judgment?   I presume we will need human judges to review the judgements given by AI Models. It is very difficult for us to accept higher court AI model, reviewing and correcting a lower court AI model’s original judgement.

The biggest hurdle is going to be us, the citizens.  In any legal case involving two parties, one party always and in many cases both parties will be unhappy with any judgement.  No losing party in any civil case is going to be happy that they lost as per some sub clause in some law text. In many cases, even winning parties may not be happy with the award amount.  In this kind of scenario, how do you expect citizens to accept an instantaneous verdict after both parties submit their briefs?  This will be a great human change management issue.

Even if we come out with some solutions to these complex legal and people problems, one technical challenge still remains a big hurdle.  With the release of many large language models and APIs, many projects are happening to train these LLMs on specific domain. A few days ago, we saw a press release by EY about their domain-specific model developed with an investment of US$1.4 Billion.  Bloomberg announced a BloombergGPT, their own 50-billion parameters language model purpose-built for finance. Who will bell the cat for the Law domain? Who will invest large sums of $$s and create a Legal AI Model for each country? Until this model is available for general use, many of the things we discussed will not be possible.

To conclude, there are huge opportunities to get business value out of the new AI technology in the Law and Justice Domain. However, technical, legal and people issues must be understood, addressed and resolved before any large-scale implementation.

More Later. Like to hear your thoughts.

L Ravichandran

To Be Or Not To Be – GPT4 Applications

Posted on September 11, 2023 - 2:35 am by

Posting on behalf on L Ravichandran

I saw this talk organized by a company called Steamship on YouTube.
 
GPT-4 – How does it work, and how do I build apps with it? – CS50 Tech Talk
 


One of the key speakers talked about various categories of applications being built using GPT-4.  No 1 is the “Companionship Category of applications”.
 
He further expanded on the Companionship category such as mentor, coach,  a friend who will give you the right feedback, a friend who will always empathize with you, etc. People are using these personas to get solace and comfort by “talking” to these companions.
 
As I was seeing this video, I was really disturbed and at the same time became inquisitive. What do we humans want? Do we want to communicate with GPT Companions or Flesh & Blood real human companions? Are we settling for GPT-Companion as the current society does not support human-to-human contact and communication?
 
The large family cluster of extended families living nearby is gone as we move away into far suburbs. The number of children per family is reducing fast. Physical games are getting substituted with online virtual games; friends are very few, and even these few friends are happy with virtual communication.
 
I know this is a question for philosophers, phycologists, and social scientists to answer. I hope they seriously look at this new phenomenon and assess its impact on human society.
 
I will conclude with the famous Shakespeare dialogue “To Be or Not to Be “. “To be a human or Not to be a human” is the new question.

AI for Sustainability and Sustainability in AI

Posted on May 7, 2023 - 4:12 am by

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

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