Article | June 18, 2020
When you first got your business off the ground, you may or may not have paid much attention to the technologies that would be available to you in the years to come—like machine learning. Machine learning was the stuff of science fiction just decades ago; now it’s practically everywhere. So, what is machine learning? Simply put, machine learning is a subset of artificial intelligence in which computer algorithms learn from large datasets in order to make more accurate predictions over time. Obviously, it’s a lot more complicated than that, but it poses numerous benefits to business owners—assuming it’s used the right way. Here are five tips for successfully adopting machine learning technologies in your day-to-day operations.
Article | June 18, 2020
The main objective of demand planning is to help businesses prepare to meet future demand. The forecasts are largely based on historical, seasonal demand patterns, not current demand signals. In fact, the main driver for future forecasting is historic sales during the same time period in prior years. The inaccuracy of demand forecasts that are based on historical data alone has often resulted in gaps between past and present situations, which, in turn, create significant business challenges. For example, seasonal supply needs based on the events of previous years and on marketing trends can lead to overstocks and increased inventory costs, stock-outs and missed sales opportunities.
Article | June 18, 2020
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 18, 2020
Imagination, creativity, and ambition are what brought us here to this modern high-tech age that we live in today. These characteristics are the reason behind mankind’s innovative creations that started from inventing the wheel in the stone ages, to the development of the first car in 1885.
“Every generation needs a new revolution”
For science, the sky’s the limit when it comes to new creations and inventions that serve mankind in their daily life tasks, or just to entertain. Whether it’s a smartphone that keeps you connected with people, a cleaning robot that keeps your floor shiny and spotless, or even a cup holder that just holds your drink inside your car. As time passes, we notice how technology and science prosper to bring new tools and gadgets to the world to facilitate human tasks. It’s all thanks to humans’ ability to imagine and create.
Now, we have reached a point where Artificial Intelligence has been introduced and is helping us do tasks much easier and faster. This remarkable technology has the ability to do what humans can in a more efficient way. It doesn’t have emotions like us but it sure can think and act like it. Therefore, as you think about it, is it possible for them to reach that point of creating, as humans have?
What Is AI Technology?
Artificial intelligent technology (also known as AI) is the process in which robots and machines, created by man, are able to solve problems, complete motor-related tasks, and think like humans by just learning from the experiences they approach. By using the method of deep learning, machine learning, and natural language processing, robots can process all the data we give them in an algorithmic manner. By doing so, they determine certain patterns and features to produce the ability to act and think like humans.
Although for some scientists and researchers, the definition of AI technology can go beyond the description I just provided you. Moreover, it’s known as robots that think like humans. You can notice all the amazing inventions originated by using AI like self-parking cars, voice-activated lights, and much more. Although does it stop there? Can more come from AI technology rather than just providing help around the house? Or when giving directions on a built-in GPS?
What I want to get at is…
Can AI Technology Create and Write Music?
It has been known across history and for generations that creating an artistic masterpiece requires passion, dedication, and emotions to bring forth a spectacular piece of art.
Great artistic minds like Da Vinci, were able to paint what to be considered today as one of the iconic paintings that symbolize art, known as the Mona Lisa painting. Another poetic artistic mind like Bach was able to create extraordinary and moving pieces of music that set new standards for musicians back in the 18th century. It is because of humans’ ability to imagine and their willingness to express their emotions through art, that they were able to originate outstanding masterpieces.
Although making music, for instance, does require that sense of passion and emotion to generate it. It still relies on basic rules, patterns, and fundamentals that are very important to know, and acquire, to make or write music from scratch. You see, any piece of music is made up of musical notes, certain chords, and a rhythmic bassline. These musical elements create what we call a melody in a song or just a full song. You can’t apply any chord to any bassline without referring to the rules of music first, because making music is all about assembling all of those key factors in a musical pattern. While following the rules at the same time. Of course, some musicians bypass those rules and still are able to create great songs. Even so, that’s just a different story from what I’m trying to explain.
Emotions in Music
When listening to any type of song, you can instantly tell if it’s a sad, happy, motivating, or horrifying song just from the lyrics. The instrumental melody in the background plays a huge factor in determining the mood of the song. Some chords are considered to provide a happy and uplifting feeling when played. Other musical notes and patterns generate that sense of sadness. Music Theory has assigned certain feelings to certain chords. So, when writing a happy or sad song you can easily pick which chord to use for the certain feeling you want to express in your musical piece. Some instruments used in songs are also related to specific feelings as well. That’s how the genre of music is determined most of the time.
Since making and writing music is mostly about knowing the rules and choosing which chord to go with which note. Then it’s possible to create a song relying only on the fundamentals of Music Theory. Also, if we consider these chords and notes as patterns and data, can’t it be implemented and processed by AI technology?
AI Technology Seen Today
A lot of modern age gadgets have been introduced to the market that uses AI technology to serve our wants and needs. Even music tech. Musical gadgets have been invented like automatic tuners that can tune your guitar hands-free, metronome watches, portable guitars, and many more. What’s truly fascinating is the implementation of this technology into next-level music inventions.
You might not know this but AI technology has been implemented on most of the music platforms you use today. Platforms like Pandora, Spotify, Amazon Music, and others use mathematical algorithms in their operating system that enable them to predict and suggest songs that might seem appealing to you. It’s all according to the thumbs-up feedback you provide for each song you listen to. It’s also associated with the constant clicks and searches you conduct for certain types of artists and songs.
It’s simply remarkable how these music platforms are able to provide the user with song suggestions and playlists that suit their music taste. This is all possible through AI technology’s ability to process the patterns in the music being played, the tempo, and the instruments used. With that data processed, it can predict the next song that will meet the user’s desire. Since AI can identify the type of music being played, then it’s safe to say that there’s a possibility it can write music as well. Actually, it already has.
Music Platforms That Create Music
Throughout history, many great musical artists have used technology and machines to make writing music an easier task. While for some it was just a source of inspiration. AI technology had been used by musicians for a long time to assist them in originating their masterpieces. Alan Turing, the godfather of computer science, built a machine in 1951 that generated three simple melodies. David Bowie used the lyric randomizer in the ’90s for inspiration. In addition, a music theory professor was able to create a computer program that was able to write new music in the style of Bach. These few examples make it clear that AI was able to assist artists in their music careers.
After a few thorough tests and analyses, AI has become a part of the songwriting process. The research has led to the development of songwriting platforms like Watson Beat, Amper, and Google Magenta NSynth Super. These platforms use the essence of AI in deep learning by processing the data given. They search for patterns in the styles, chords, and other musical elements between songs to produce new material in the end.
Songwriting platforms, like Amper, allow anyone without the musical knowledge or experience to create a full song instantly. The process is quite easy. All you have to do is pick a genre, mood, and tempo while Amper takes care of the rest. This program has been used to create music for podcasts, commercials, and videos for companies. Still, it hasn’t been able to produce a hit song that will reach the top spot in the music billboards. My guess is because it lacks passion and emotion when creating a song.
Will AI Technology Take Over the Music Industry?
While a lot believe that someday AI Technology is going to backfire on us and take over our jobs. Some say that if they do take over everything then more jobs will emerge from it in the process. It’s a conflicting discussion.
What has been made clear though, is that today’s modern age technology has been able to create and write music just like humans can. It’s faster and efficient. Even though it might seem that AI will take over the music industry. It still doesn’t have that emotional side of making music. Furthermore, it’s been proven that even music writing platforms, like Amper, don’t have the ability to create a hit number one song. Or one that will catch the attention of millions. Although they can create music for marketing, promotional, and commercial purposes easily.
In conclusion, Artificial Intelligence is truly a mind-blowing invention. Having it helps us with our everyday life tasks and daily routines. Nevertheless, making it write music is also remarkable and sets high standards for technology nowadays. We don’t know what technology has installed for us but it’s no doubt making the world a better place.