Ian Head (https://www.linkedin.com/in/ian-head-245168/) 

Researcher- Rudrani Bose (  https://www.linkedin.com/in/rudrani-bose-a2a1441b0 )

Many Governments have AI Strategy documents in place that we can expect to lead to legislation and major investment in the next 3 years. To reach global markets and avoid confrontation, AI companies should be aware of government activity in this field. 

Artificial Intelligence presents limitless growth opportunities. Yet uncontrolled growth could quickly convert into threats if unchecked by regulatory standards. As a result, more than 20 countries across the world, along with groups like the World Economic Forum and the European Union have released AI strategy documents. Many of these can be expected to result in legislation. 

Although the United States of America released documents on artificial intelligence back in 2016, it was Canada in 2017, which became the first country to release an AI strategy. 

Since there are numerous policy documents, it is imperative to conduct a comparative analysis of these strategies, using specific standard parameters, to identify similarities, differences and the overall impact of AI on global populations. The following are snippets of some government’s  AI strategy and policy-making initiatives :

• The 2017 Federal budget of Canada highlighted the five-year Pan-Canadian Artificial Intelligence Strategy, making it the first country to do so. 
• On the 11th of February 2019, President Donald Trump announced the United States national strategy on artificial intelligence, called the American AI Initiative. With a vision similar to the Canadian approach, the American AI initiative aims to promote and foster a national culture using artificial intelligence technology and innovation. 
• In 2017, Singapore launched the AI Singapore program to invest up to $150 million (USD111 million) in artificial intelligence over five years. The program brought together stakeholders from AI R&D, AI-based start-ups, and companies developing AI products to collaborate for the future of the digital economy. The National AI Strategy of Singapore was launched in November 2019 towards responsible adoption and innovation of artificial intelligence in Singapore, targeting the improvement of logistics and transport, smart cities, health, education, estates, safety and security. 
• In the federal budget of 2018-19, the Australian government earmarked $29.9 million (USD21.7 million) to develop the nation’s AI and machine learning (ML) capabilities, to be utilized by the Department of Industry, Innovation and Science, the CSIRO and the Department of Education and Training. (comp)
• In 2018, at the World Economic Forum at Davos, the then UK Prime Minister Theresa May announced the UK’s largest-ever investment of in research and development of digital technology

The key highlights of the AI strategy and policies from five leading governments around the world are mentioned below:

USA

· Encourage investments in AI research and development to unleash AI resources and remove barriers to AI innovation

· Upskill and train an AI-enabled workforce 

· Promote an international environment fostering responsible use of American AI innovation

Through this initiative, the United States hopes to nurture a climate of respect for freedom, human rights, intellectual property rights, the rule of law and equal opportunities for all in a new future with artificial intelligence (Whitehouse).

One interesting observation to make here is that in the USA there is no right to privacy. That is why European GDPR laws are so problematic to USA companies. USA companies expect to be able to do what they like with citizen’s data. The EU thinks otherwise.  In September 2020 Facebook threatened to quit Europe over this issue [https://www.computing.co.uk/news/4020505/facebook-threatens-quit-eu]. 

Singapore

· To be a leader in AI by 2030 through developing scalable, impactful AI products to deliver value to its citizens

· Build an AI-ready workforce to prepare citizens for the technology change. 

Australia

In 2018, the Government of Australia released a digital economy strategy – Australia’s Tech Future – highlighting the vision for governments, businesses and society to collaborate and reap the maximum benefits from digital technology. 

Australia’s concept of the future of AI involves the following objectives:

· To explore the future of AI in Australia, develop capabilities and build adequate policies to match capabilities

· To enable digitalization and solve challenges in the sectors of health and welfare, energy, education, transport, infrastructure and environment

· To improve the safety, efficiency and quality of processes in Australian industries

United Kingdom

The United Kingdom is ranked #1 in the Oxford Government AI Readiness Index, in terms of the ability of governments to take advantage of the benefits of automation. The Sector Deal articulates its visions in the development of AI as: 

To develop an industrial strategy through a focus on the five foundations of productivity:

-  Ideas – to be the most innovative economy in the world, 

- People – to ensure employment and better greater earning power for the citizens, 

- Infrastructure – boosting UK’s Infrastructure, 

- Business environment – To transform into a progressive and suitable business environment 

- Places – to nurture prosperous communities. 

https://www.weforum.org/agenda/2019/08/artificial-intelligence-government-public-sector/

http://www.unesco.org/new/en/media-services/single-view/news/canada_first_to_adopt_strategy_for_artificial_intelligence/

https://www.cifar.ca/ai/pan-canadian-artificial-intelligence-strategy

https://ec.europa.eu/commission/presscorner/detail/en/IP_18_3362

https://www.whitehouse.gov/ai/executive-order-ai/

 Contributing researcher: Aparajita Bandopadhyay

Why is language still the toughest problem to crack, in AI?

• The core paper on GLUE - the NLP/NLU benchmarking system for General Language Understanding Evaluation (GLUE)- mentions that human ability to understand language is general, flexible and robust. Well, several NLP/ NLU algorithmic techniques and systems developed thus far, have been neither of these. 
• Even after GPT3 having 175 billion parameters, the articles generated by it still requires relevance and context testing of the output, and consequently a huge amount of editing. Claiming GPT-3 to be the Nirvana of NLG and the ultimate step towards AGI in the field of language is arguable.  The recent claims by a well-known media company about using GPT3 to generate a perfectly credible article for their newspaper, triggered a big debate amongst the AI technologists, practitioners, and marketing folks. 

Do we determine what our words mean, or do words determine what we mean?

Though this rather childish-looking question may seem to be something which only characters as truculent as Tweedledum and Tweedledee would care to dispute ad infinitum, upon a little further analysis, it can really throw us into a confused dilemma.

• What is language? The Oxford dictionary defines it as 'the method of human communication, either spoken or written, consisting of the use of words in a structured and conventional way’. Many writers have considered it to be far greater than the aforementioned definition, and some consider it to be an omnipotent and utterly ingenious device which can alter the course of our lives.
• Languages have not only been shaped by us, but have done the converse, too; words have cheer-led, misled and led us to many a strange time and place in the course of our past. Though it is not evident to most at a first glance, languages can serve as a radically deterministic power on the way we think. Most foreign learners, upon being subjected to the learning of a foreign tongue, express their intense dislike for dense and complex grammatical modules, as well as intangible scripts; we are often led to wonder what strange concatenation of events must have led to the development of such a volatile and complex thing as language, which was originally designed for the ease of communication.
• Almost all languages share the common essentials- they have distinct categories for the grouping of verbs, nouns and adjectives, as well as tense. The sheer fact that so many languages have so much in common shows us the astounding uniformity underlying human thought; only when contrary examples are cited do we recognise this; consider, for example, a certain language called Hopi, which bears absolutely no references to time! Several languages,including Indonesian and Mandarin lack discrete verb tenses; but to think of living without the concept of Time, in all its abstraction and paradoxical regularity,seems to be impossible. So many aspects of time-its linearity, its irreversibility, its ability to serve as a dimension,of sorts-have been embedded in us from a young age, via our languages. If time travel were possible, we would have very strange tenses indeed!

Noam Chomsky was one of the first linguists to suggest that the proficient usage of any language requires a certain instinct, and even, perhaps a certain genetic code. Yet, several languages are unique in their own ways; German and Russian, for example, are largely devoid of a distinct continuous tense; French allows the usage of double negatives to mean negatives!.

Contributing researcher: Aparajita Bandopadhyay

Ground-rules of AI & HI Part -1: Basics of Neuroscience- A young Researchers' Point of View

From the 1950's, since the phenomenal paper of Turing got published- on the Imitation Game, almost all definitions of AI have included this common notion that AI attempts to somewhat 'mimic' the ways of human intelligence. But, do we still know exactly how human intelligence works in the first place, so that we can mimic it in machines?  
In the past 2 decades, thanks to huge progress in physics and engineering and MRI scanning technologies (e.g. fMRI of human brains during various activities- ref. Michio Kaku- Future of Mind), a lot has been revealed about how human brain works- from a neuroscientific perspective. Here is a VERY SMALL collection of some of these basics.

Q:What are synapses?

• A synapse is basically the gap between the ends of the axon terminals of two nerve cells which serves as a transmitting medium, or sort of connection, between them. Every time we learn something new or practice something, new synapses get formed and old ones get strengthened. Synaptic transmission via transmitters and receptors occurs mainly in two ways:
• Electric synapses:Two neurons can be coupled together by 'gap junctions'. A gap junction is a form of protein that allows ions and other small molecules to move between cells. Neurons connected via gap junctions are found in areas where they need to be very well-connected and coordinated, such as the hormone-secreting regions of the brain, like the hypothalamus.
• Chemical synapses: These synapses don't allow small molecules and ions to move between cells. Hence, several neurotransmitters(recall part 2) are required for transmission of signals here.

Q: How can we manufacture and strengthen new and old synapses?

• In order to strengthen old synapses,we must repeat the task we performed, or a task similar to it. This helps strengthen the connections between neurons and gyri and makes the brain perform the task with much greater efficiency afterwards.This shows that with adequate practice, we can master anything to a level that almost seems like we are 'effortlessly' performing a task. The more often you perform a similar task, the less energy your brain will spend on it and will let you develop new skills with relative ease,too! 
• The more our brain learns,the more will it learn to learn?
• Before practicing a new and important skill, try to adjust your mindset so that it tolerates and accepts new ideas and opinions. Do not forget old information- but be open-minded about new information, and try to look at information from different perspectives to understand it better. 
• Using music, mnemonics, acrostics, rhythms and patterns is a great way to revise, practice and learn new things. Constantly practice thinking about something important by looking at it from different angles, analyzing it well and also try to explain it to someone else. All these exercises will make your synapses super-strong!.

Contributing researcher: Rudrani Bose  https://www.linkedin.com/in/rudrani-bose-a2a1441b0

Standards and guidelines for the regulation of artificial intelligence are rapidly emerging and are more important now than ever before. The technology of artificial intelligence is not only new, but it is a work in progress, ensuring that advancements occur every day. Fierce competition for the development and deployment of AI technologies is likely to emerge in the future, necessitating the use of legal standards to act as a deterrent to AI being employed for unethical purposes. 

Some of the existing legal standards governing artificial intelligence are:

•  IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems- 
• The IEEE Standards Association has the IEEE P7000 series of working groups,  in AI standards, comprising of 14 groups, to address issues at the meeting point of technological and ethical concerns. 
• European Union’s General Data Protection Regulation (GDPR)

A key challenge of in actually implementing and following these standards on ground is that - these standards focus on technical interventions and checkpoints, e.g. on AI data usage, algorithms and applications. They don't cover guidelines on applying these standards on the ground, in terms of people- e.g. training & awareness about risks of non-compliance, process of auditing to check enforcement and adherence to the standards, and the business outcomes i.e. the impact of implications of following the standards. 

Amazon AI Conclave, and the Union Budget Next Monday Morning: Emergence of a Perfect Equation?

Amazon just concluded their Virtual Conclave on AI, late last week. This was the 4th edition of Amazon  AI Conclave, because of COVID guidelines it had to go virtual this year. 

While I was writing this blog on my key takeaways from the Amazon AI conclave, I was also listening to the live telecast of budget, presented by our Finance Minister.  She was clearly listing down the financial and economic priorities of our country, to push it towards a growth path that was hopefully faster than the ones some of our smaller and hence nimbler Asian neighbors and friends have already started enjoying (e.g. Bangladesh and Vietnam).

A key benefit of my highly practiced multi-tasking skills suddenly dawned upon me- Connecting these dots. The key messages from the Amazon AI Conclave and the Union Budget priorities are so much in sync! Whatever the honorable FM was saying, I heard the technical equivalents of those initiatives and narratives, just 2 days before, in the Amazon AI Conclave.

Directionally, both were spot on. However, as all the experts were mentioning in their budget analysis,  the devil lies in the details, and in execution. Great strategies have failed far too often, due to unimaginative, conservative implementations. It’s one thing to say, and completely a different thing to actually do, and achieve demonstrable and proven outcomes.

Bridging this mission-critical gap between ambitious visions and average/ half-baked execution, is the task at hand- both for the country and for AWS, for India and the world to become the connected, ubiquitous, global powerhouse of AI, data, cloud- the three key pillars of the Brave New World:

1. What stood out for me from the very first session of the AWS Amazon AI conclave, is that the leadership has already started stretching their goals and execution plans in the right direction, in terms of moving beyond just tech-speaks and talking consistently about Customer Outcomes i.e. proven execution and measurable, demonstrated results. Everything that were spoken, had proof points from case studies that were already successfully executed and delivered in real customer landscapes, and had already generated proven results, e.g. bringing down ML model-build (hence productionization) times from years and quarters to 1-2 weeks!
2. The global AI-data tech supply-side shifts from labelled data and supervised learning to unsupervised and deep reinforcement learning algorithms were explained with brief, specific, relevant contexts of client applications and realized business outcomes like retail shrinkage prevention, and not just outputs in terms of model performance, AUROC and accuracy parameters.
3. The other key take-aways were multiple reinforcements from the Amazon leadership team-members on the importance of harnessing an “ML culture”. This is why the patented framework of AI-SWIT‘C’H includes ‘C for Culture’ as a key dimension, for operationalizing AI and intelligent automation in organizations. The leaders explained with ample examples both from within and outside their organizations, across clients and partner ecosystems, on how certain culture change levers and initiatives are bearing fruit now, e.g. ubiquitous access to quality-validated ML training programs & self-learning materials, the AWS ML University, and democratization of access to data and tech-stacks for AI.  [For details: ref Amazon Machine Learning Solution labs <https://aws.amazon.com/ml-solutions-lab/ ] Zomato, Edelweiss Tokyo life Insurance and Jubilant Food works (Dominos India) are some exemplary instances of ML deployments where Amazon ML Solution Labs led the deployment effort for ML model building.
4. The 6-step approach and the importance of prioritizing on the right projects absolutely resonated with the “Minimum Viable Strategy (MVS) for AI” guidance that AISWITCH has published in 2021 (https://aiswitch.org/ai-practice-tsp).

While the proven success stories were many, there are obviously even greater opportunities to explore and expand on:

1. Having worked very closely with the AWS tech teams during the early days of setting up the Wipro HOLMES AI & Automation Ecosystem, there are so many wonderful assets that we tried hands-on from the AWS ecosystem, which did not get adequate mind-space of the analyst audience. This was probably 1- for want of time, 2- also due to the known fact that the analyst community usually don’t have much hands-on tech exposure to the “Deep AI space”:
2. Sagemaker examples were mentioned, DevOpsGuru, CodeGuru were touched upon, for the hybrid “geeky analyst” folks like me, I guess. Healthlake – the healthcare datalake construct was briefly explained. These were like taking a ride in the Tomorrowland’s Carrousel in Disney Orlando, but the carrousel got stuck mostly in the current scenario.
3. AWS is the global Father Figure of Cloud, being the First Mover in bringing the cluster/ grid computing technologies out of the labs and making them commercially feasible in making businesses agile, elastic and digital (thanks to GPUs usage in a novel way, EC2 and the storage clouds). There are great stuff in SageMaker e.g. Ground Truth and Data Wrangler- these capabilities must be flaunted and brought forth so that business users i.e. the target citizen developers of ML get early exposures on what the right questions are. Of course these topics were extensively covered in Day 2 which was the technology edition by design. But, quick exposures to some of these, at least in terms of demonstrable "Ease of Learning", could have benefitted the business leaders and analyst folks who could then enthuse the citizen builders in their respective functions, in a more informed manner. 
4. AI-readiness is a leading indicator, not a lagging one. Hence, all the conversations, be it in customer, partner, analyst or tech research-space, should be feasibly ambitious & forward-looking. Most of the AI tech capabilities are already here, except in spaces like generative, super AI, and the 'Catch 22' scenarios in language corpus, with OpenAI's GPT-3 & Google's SMITH and BERTs & their domain-specific versions, and BIGGANs' semanticity problems, etc. 
5. Discussions must also include topics like Quantum ML (e.g. IBM's Quantum cloud equivalents- for which ML problems do quantum techniques make sense?) and Green AI, meta-learning, zero-shot learning, RT AI, XAI and data bias reduction techniques. Knowledge exchanges on AI should aggressively promote innovative lateral thinking, e.g. how the unsupervised/ reinforcement learning techniques produce indirect "labelling bias reduction" benefits, beyond just improving performance and precision.

Net net, using India as the “Most Complex Problem” statement and context, Amazon has more than sufficient deep tech first-mover capabilities to build and test these prototypes for our immediate future here, and then scale up quickly, for the world. 

Technology-wise, AWS and other hyper-scalers are the Super Integrators of elastic data-algorithms-infrastructure, in the way of cloud and federated & edge AI. As the global IT services hub, India has a huge pool of service knowledge & curable datasets (can be masked for PII), and trained tech manpower that is agile i.e. that can willingly and quickly switch to AI-ML capabilities. 

If this is not an example of a Perfect AI demand-supply equation, then what is?

On 2nd November, TOI and Moneycontrol et al carried this story about potential overhauls at Wipro under the new leadership of Thierry, a BFSI veteran from CapG, a leader with the transparency and clarity that's typical of many European leaders (not stereotyping, just a generic observation :)). These articles also hinted at the possible change-paths Thierry may take for Wipro, similar to what Brian did for CTS, some time ago. 

Observationally, looks like it. To improve margins, not only TWITCH but even the global co.s are ruthlessly chopping off layers of legacy corporate fat that has made several of them rather ANTI-AGILE, if anything.

Finally this entire SP industry seems to refocus right- on GPR-Value (e.g. direct or indirect Growth/Profit/Revenue-Contribution-Per-Employee), and is using AI and intelligent automation within their own houses first, as apt instruments of CORPORATE LIPOSUCTION!

Since Y2K days, when KLOCs of COBOL were being updated by sheep-herds of trained engineers (layers of managers were the shepherds), the whole SP industry focussed on TnM- throwing more bodies towards all client problems!

Sadly, even now, many research firms still weigh the brawn more than the brain: Quantity of manpower 'certified on tool X', vs. Quality of Talent, or No. of IP's these new sheep-herds are producing.

This is the new double jeopardy (also double accounting) problem of AI, creating new layers of confusion matrix & resultant inefficiencies. This time: Still too many men, PLUS too many machines with varied IQ- haphazardly thrown into the already-horrifying mix. Cutting unnecessary flab is a critical and lifesaving surgery that many companies in this new conundrum need at this hour.

However, the intent defines the extent. Hence, instead of equating Thierry's change attempts for Wipro only at face value as an exact replication of what Brian did for CTS, we should look deeper. Intent-wise, Thierry is focussing on simplification and easily measurable, demonstrable VALUE metrics, whereas Brian decidedly focussed on COST-CUTTING. Their leadership challenges are different, company culture and realities are very different. What works at one won't get the other exactly there. CTS, while becoming more cost-efficient, also did lose a lot of great talents and experienced business leaders and client partners that they were known and strongly differentiated for. Given Wipro's core leadership legacy of Premji and Rishad, and the loyalty of some of their great leadership minds towards the owner family and the enterprise, surely that will not happen for Wipro, no matter what changes are brought in.