Article | April 20, 2020
One of the measures to reduce the spread of coronavirus in the current crisis is social distancing. This entails that when outside people should stay at least 2 metres (6 feet) away from each other at all times. While this is not a difficult requirement to meet for most people, workers in industries that have been identified as essential and continue to work during this current period of quarantine will not find it as easy to meet this rule. This is why Landing AI has created an AI-powered tool to ensure that people are maintaining a safe distance from each other.
Article | August 14, 2020
Last week concluded another valuable Black Hat USA event – although held entirely virtually this year, the impact of the research, trends, and knowledge shared was as strong as ever. From new industry trends such as extended detection and response (XDR) to current event security considerations and best practices to detect and mitigate some of the most prevalent and challenging threats – the content did not disappoint! In case you missed them, below are a couple sessions that grabbed headlines and high audience engagement:
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 | August 13, 2020
With Google Analytics, you can determine where the clicks to a certain website or webpage come from. However, this analysis isn’t the most precise method. For example, you can only find out whether traffic came from a specific source such as Twitter, but not whether the tweets from your own company were responsible for this linkage. In other words, you won’t be able to tell exactly which version of your call-to-action generated more clicks if both versions linked to the same URL. But there’s a solution: using UTM parameters.