Article | August 14, 2020
Okay, I assure you that this blog is not about a cheesy reality TV show. However, there is a group of real Wi-Fi reality stars at Extreme Networks that get very high ratings, but without all the drama! I am talking about all the CWNEs that work here at Extreme. For those not familiar,Certified Wireless Networking Expert (CWNE) is the top-level certification offered by the CWNP program. To become a CWNE, a WLAN professional must pass four very hard exams about 802.11 wireless networking administration, security, analysis, and design. But keep in mind, passing four tests is only the beginning. All CWNE applicants must establish many years of wireless networking experience, meticulously document involvement with numerous WLAN projects and seek endorsements from respected Wi-Fi industry professionals. The CWNP Board of Advisors must approve all CWNE applications.
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 | December 21, 2020
Machine learning — a branch of artificial intelligence that gives computer systems the ability to automatically improve and learn from experience — has been making serious waves for the last few years. More recently, though, the applications for smartphones and other small screen experiences have started to take shape, driving the way millions interact with their mobile devices.
Yes, Your Mobile Devices are Becoming Smarter
So what do these innovations means for your business? Machine learning can, essentially, make your smartphone “smarter” by improving a host of functions and processes instantly. In fact, most smartphones are already using some type of machine learning or intelligent automation application that aids mobile devices in becoming more efficient and effective. Predictive text messaging, for example, is one such application that’s already become part of the mobile vernacular — chances are, you use it daily without thinking twice.
As a whole, businesses are ramping up their machine learning investment, meaning we’ll be seeing more of this technology — and more accessible versions of this technology — in the coming months and years. For each generation, there’s an added level of intuitiveness when it comes to mobile technology — your current smartphone is smarter than the computers that helped bring man to the moon, in many ways. From that end, how advanced will our mobile devices be in another 10 or 20 years? Smartphones could be paving the way for Robotic Process Automation (RPA) and evolving the very way many industries work.
What’s Next for Mobile Machine Learning
Historically, machine learning requires a tremendous amount of power — power mobile devices simply didn’t have. However, businesses can now install special chips in drones, automobiles and smartphones enabling them to consume 90 percent less power. As a result, mobile devices — even without an internet connection — can perform a variety of once-complex tasks, including:
Virtual / Augmented Reality
Smarter Camera Functionalities
Improved Device Security
Going forward, envelope-pushers are driving towards even bigger, better, more sophisticated applications — think motion control and navigation, diagnosing and analyzing sensory data and more. Interactivity or perceptual interfaces are also capabilities that the new applications are expected to be equipped with, giving mobile devices seemingly endless capabilities.
Due to these unique benefits, machine learning on small devices is clearly becoming a priority for businesses.
Article | December 20, 2020
Staying relevant and cutting edge in the business world is a struggle for businesses in any industry. Technology, including intelligent automation, is continually evolving. Businesses must change with it in order to be competitive and successful in our current macroeconomic world. The use of intelligent automation tools can help grow your business and improve how your business operates, reducing your operating costs while improving your production time.
Reducing Human Error
One of the most important benefits that intelligent automation brings to any business is the reduction of human error in the work place. People are naturally affected by their daily lives and outside influences. If a worker, for example, came into work tired or unwell, his or her job performance will likely suffer, the risk of human error becoming greater. Automation software cannot be affected by time of day, mood, lack of sleep, etc., allowing it to be completely consistent in performing the task it was programmed to do.
Additionally, humans need to be taught new tasks and require practice in order to master them, robotic process automation can be updated and perform the tasks instantly.
Max Yankelevich, founder & CEO of WorkFusion, says it best “Robots need only eight to 12 weeks to take over a back office function that humans take years to learn.”
In terms of business benefits, utilizing intelligent automation tools ensures performance consistency that will ultimately improve the overall quality of work, also allowing human workers to focus on higher priority and more important issues that require critical thinking.
Keeping Jobs Local
Employers have often ventured overseas to hire workers in other countries who can then perform basic tasks at a reduced wage, when compared to local employees. The bottom line can be better for these employers in the short-term, though working with outsourced employees means sending money overseas and trying to manage workers on another continent. Typically, over the long-term businesses that outsource overseas can experience unforeseen issues and costs due to the complications with depending on a foreign workforce.
With outsourced jobs being performed by intelligent automation tools businesses can focus on hiring skilled workers from the local market for the upper levels of the workforce.
Return on Investment
Perhaps the most intimidating factor in implementing intelligent automation within your business is the upfront cost. Putting money into something new is not a leap everyone wants to make. Intelligent automation, however, is not a gamble. Research shows that companies who use are able to automate around half of their tasks, increasing process time by fifty percent. Completing tasks more quickly means companies can take on more tasks without spending additional time on them. Depending on the industry, having jobs done quickly can mean increased revenue.
If performing redundant tasks quickly and accurately will not improve your company’s revenue, just simply utilizing intelligent automation tools certainly will. Such tools do not need pay, employee benefits, and can work overtime, the return of investment becomes apparent when considering all the expenses intelligent automation does not require.
Intelligent automation tools offer businesses unparalleled levels of productivity, efficiency, and value. Companies will want to avoid the risk of falling behind by adapting with the modern technology, the advantages of utilizing intelligent automation tools can lead companies to developing new business strategies they could have never even possibly conceived of previously.