Article | June 29, 2021
Depicted as a natural predisposition to form groups of work, teamwork has been popularized through history as a central feature of organizational change programs that advocates empowerment and disruptiveness. The suasive force of discourse regarding the ineluctable essence of teamwork as a tradition and custom founded on some inclination for humans to work cooperatively, create a set of “rituals”, conventions and practices which invite to innovation, flexibility and creativity.
Teamwork as “human nature” was a common thread all through history and management literature. The team-based nature of early human activities can be traced to hunter-gathering in societies where orality was the prime source of communication. The locus communis was the collective memory (facts, rules, code of conduct, religious beliefs and practical knowledge). The pneumonic function of the verse will fulfil a didactic function as a way of memorizing any content in order to systematize a conceptual theoretical primitive language. In preliteracy times, doctrines and their conservation were highly dependent of the spoken word and memory (Havelock, 1957, 1992). Thus, in an oral culture experience is “intellectualized” mnemonically (Ong, 1982). In a sociobiological perspective, aspects of teamwork behaviour allude to a biologically determined “natural history of species”.
According to Katzenbach and Smith (1993) “teams- real teams and not just groups that management call “teams” should be the basic form of performance for most organizations, regardless the size”. This statement clearly sets the basis for the team as a natural building block of any organizational design. Buford (1972) in a comprehensive study of Ancient Greek and Roman craftmanship interpreted teamwork in a very familiar approach we understand it today: collaborative work, multiskilling, mutually interdependent tasks. There were technical divisions of labour based on skills, the relationship between mentor and apprentice and so on. The greatest craftsmen were expected to be versatile in different skills, but the coordination of work efforts was left to the so-called professional cadre of engineers, architects and masters.
With the advent of Capitalism, the massive growth of the economic activity claimed for reorganization. A new form of discourse emerged, our prehuman origins and modes of communication becoming codified and formalized as the scientific disciplines of evolutionary biology, economics and linguistics respectively (Foucault, 1972). Within the economic discourse, there was a creation of a distinct managerial object, which opened new domains of knowledge and professional practice.
The mythical traditions of teamwork replicated in today’s contexts and the “tribal” notion of team popularized by Codin (2008) paves the way to concrete changes in the form we perceived our working environment. The analogy of team as “family” so common in the corporate world which in its essence represents our first experiences as a community is not a happy term anymore, since in a manner it could go against the interests of today’s organizations. Therefore, in building a healthy sustainable workplace culture teams cannot be perceived as family. Teams have a commitment to a common goal, clear expectations and performance.
The MetaQuant: From siloed work to interdisciplinary collaboration
With the paradigm shift to automation, organizations are taking actions that promote scale in AI through the creation of a virtuous circle.
The central overarching question is: Are traditional ML teams good enough to develop models able to achieve long lasting competitive advantage?
“In a world spinning around AI, competition among institutions seems to be fierce while mayor obstacles appear on the way: recruiting top talents is not only time-consuming but also high-priced, or just trying to find a balanced approach to talent, meaning "reshaping" the old-school computer scientists into quants, is critical in terms of AI implementations. The big winners: those firms that integrate AI with human talent” (Litterio, 2020: 167).
Successful machine learning (ML) projects require professionals beyond engineering expertise. AI has the biggest impact when it is developed by dynamic creative cross-functional teams. The move from functional to interdisciplinary teams initially brings together the diverse skills and perspectives to build effective tools.
In order to bring theory into practice, and in the need of a novel conceptual framework design, I have coined the term MetaQuant.
The MetaQuant is a new breed of market players, who “translates human language into signals” and "reads" the data from a holistic perspective identifying patterns within the linguistic and symbolic constructs. The MetaQuant is the linguist, the semiologist, the sociologist, the cognitive psychologist and the philosopher or rather a combination of these intertwined profiles which will fuel the potential for information advantage providing a unique core differentiator transforming data into knowledge. In this sense, the MetaQuant has emerged as a crucial component of any AI model paving the way for a novel insight where hybridization is critical. The formula for a successful organization in a discovery-driven environment is the MetaQuant + The ML team. And eventually the Quantum Computing Expert. Finding the needle in the haystack can be a competitive difference maker.
Creative thinking, actionable insights, collaboration, proficiency, flexibility, shared vision and training are the ingredients for an elite team.
It is vital for organizations to establish workflows that empower everyone to play a role in order to move projects from test to deployed AI/ML. Yet, knowing how to do ML is not the same as being proficient with it and knowing how to implement a ML model end-to-end is not the same as using ML creatively to build solutions to real-world problems, to explore and assess potential applications specific in competitive contexts.
Ideally, when selecting members for your elite team, it is advisable to make a first distinction between those who wish to do research in ML from the ones who wish to apply ML to your business problems. Both are of major importance alike. The instreaming of new talent brings in novel ideas which can positively impact the work culture.
Demonstrating flexibility is a significant asset. Since ML projects may encounter all kinds of roadblocks, being able to easily change tactics to overcome obstacles without getting frustrated or losing sight of the end goal is key to deliver projects.
Mentoring and inspirational leaders is greatly valued when designing a ML team. An exceptional team leader is the one who shares a unique perspective and knowledge. Experience in the field is a substantial source of wisdom within the organization. Having a passion for diversity of input and fostering a healthy culture of support distinguishes average from excellent ML teamwork.
Educating everyone is the dictum to become an AI-first institution.
To ensure the adoption of AI, organizations need to educate everyone, from top leaders down. To this end most are launching in-house programs which typically incorporate workshops, on-the-job training to build in capabilities. Some others, and which reflects a common trend today, opt for partnerships with renowned academies or prefer the outsourced modality “training as a service” program or a bootcamp.
For an A-team, it is critical to make a mark in the ecosystem through journal publications, book chapters, white papers or lecturing in conferences. Disseminating their work and findings through meetups, workshops, and seminars is a must for building a thriving culture that promotes exchange and cross-fertilization of new ideas and technologies in a substantial way. Systematicity and coding belong to the ritualistic change of conscience.
Article | June 29, 2021
2020 has been an unprecedented year where we have seen more downs than ups. COVID-19 has impacted every aspect of our lives. But when it comes to digitisation and Artificial Intelligence, we have seen some impactful developments and achievements. As we approach the end of 2020, it is worth to look back at these AI stories to highlight the truths and discuss what it means for AI future direction.
The Great Truth:
Artificial intelligence played a crucial role in the detection and fight against COVID-19.
Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients were diagnosed thus enabling the medical team to follow the necessary protocols. Another application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments.
COVID has highlighted the need to deploy intelligent autonomous agents. As a result, we have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules.
Another major AI contribution in the fight against COVID-19 is in the area of vaccine and drug discovery. Moderna’s vaccine that has been approved by US Food and Drugs Administration has used machine learning to optimise mRNA sequencing.
The above is a proof that AI can make great contribution to mankind if it is used for “good”.
The Glowing Truths:
Some impressive AI results have been achieved. However, to leap forward a holistic and sustainable approach is needed.
2020 has seen some great AI achievements and leaps forward. The first example is Deepmind’s AlphaFold. The model scored highest at the Critical Assessment of Structure Prediction competition. The algorithm takes genetic information as inputs and outputs a three-dimensional structure. The model has impressively addressed a 50-year-old challenge of figuring out want shapes proteins fold into known as the “protein folding problem”.
While Deepmind’s AlphaFold is a great achievement, it is noted by some scientists that it is unclear how the model will work with more real-world complex proteins. Thus, more work is needed in this area.
The second example is OpenAI’s GPT3. The model is a very large network composed of 96 layers and 175 billion parameters. The model has shown impressive results for several tasks such as NLP questions & answering and generating code.
However, it is noted that the model does not have any kind of reasoning and does not understand what it is generating. Furthermore, its large size makes it very expensive. It is also unsustainable carbon footprint wise; its training is equivalent to driving a car to the moon and back.
While both AlphaFold and GPT3 models are both impressive achievements, there are some philosophical challenges/ questions that need to be addressed/ answered. The first question is about games/ simulated worlds vs. real world examples. Most often algorithms/models succeed in simulated world but fail in real world as the environment is more complex. How can we close the gap? How can we make the AI models succeed with complex tasks? I guess the first step is to apply AI to a real-world example with varied complexity levels.
The second question is about the structure and the size of AI models. Do models have to be big? Can we come up with a new generation of algorithms/ models that are smaller is size and have more efficient computations? Well to answer this question we have to take a pause on deeplearning and explore new venues.
The Gross Truths:
Ethics and bias remain the main drawbacks of Artificial Intelligence.
Over the last year, we had several prominent examples of AI ethics and bias issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology.
The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm.
In the absence of regulations and tightened frameworks, ethics and bias will continue to be the main concerns surrounding the use of artificial intelligence.
Looking into the future, AI adoption will continue to accelerate, and we will probably see more breakthroughs achieved by only if we start looking at the subject in a holistic and sustainable view. Focusing models on real world problems and reducing the models carbon footprint will be a major step forward. We need to move away from thinking that “more” is always “more”. Sometimes “more” is “less”.
Article | June 29, 2021
The NISI Group (NISI) is a medical technology business headquartered in Hong Kong, specializing in the development, manufacture, and sales of innovative surgical and diagnostic products. Its corporate mission is to become a world leader in noninvasive surgical innovations.NISI first used an NVIDIA DGX-1 as a high-performance computing node to help with its survey of research courses. Because the initial training dataset was not large, the simulation was done entirely on the NVIDIA DGX-1. However, NISI was collecting more labeled medical images for training its new network and sourcing data from several hospitals and clinical centers, and its data volumes were growing exponentially.
Article | June 29, 2021
The book Design, Launch, and Scale IoT Services classifies the components of IoT services into technical modules. One of the most important of these is Artificial Intelligence (AI). This article is intended to supplement the book by providing insight into AI and its applications for IoT. After many years in the wilderness, AI is back on the hype curve and will change the world again. Or, will it? AI has always been interesting, but what has changed to justify the current hype?