Vectorspace AI- The cost of Understanding
“Knowledge is knowing a tomato is a fruit. Wisdom is knowing that they don’t belong in a fruit salad.”
In 2002 a baseball team became one of eight, in the history of the sport at that time, to finish with a seasonal record of more than 100 wins. They were a small market team, meaning they were on the low-end financial spectrum in comparison to their competitors. They won 20 games in a row during that season. The first team in 100 years to do so at the time. The greatest winning streak to date in the sport.
This was the Oakland Athletics.
In the year 2000, Reed Hastings met with a company and was, in his own words, laughed out of a business meeting when he proposed to sell the start-up he had created in 1997, for 50 million dollars. In 2010, the same company that had laughed at him filed for bankruptcy, and that same year Reed Hastings took the start-up company, which a decade ago he had tried to sell, to the open market. Reed Hastings is the co-founder of Netflix. That company that filed for bankruptcy was Blockbuster. Netflix in 2021 is valued approximately at 30 billion dollars.
In 2015 a football team coming off survival from battling relegation the previous season, a process in which three teams with the lowest points recorded that campaign are demoted to a lower level of competition, began the new campaign with odds of winning the league at 5,000/1. They won nine months later. They were the second-lowest valued team in the league at the time the season began and had sacked their manager that same summer.
This team was Leicester City.
These three events are completely unrelated. They occurred at different periods of time, different events, locations, starring different people. The knowledge that you gained from learning about these events feels wasted because you can’t apply it to anything, it hasn’t made you wiser nor become a tool that has enhanced your life…
Well, that’s all depends on your perspective.
What if I said there was a common factor that caused these scenarios to play out the way that they did. A hidden relationship between the knowledge presented to you today. I’ll give you a hint. Netflix didn’t kill blockbuster, the Oakland Athletics didn’t outcompete their competitors, and Leicester City proved it wasn’t a miracle story but more like they cracked a hidden formula.
The hidden relationship between all three of these stories is… data. Nothing more nothing less.
Each of their competitors was exposed to the same type of data available to them, throughout these three successes. The only difference was how they used that data and the angle they looked at it from. Reed Hastings looked at the landscape of the internet and the consumers utilizing Netflix and adapted to make it more accessible and user friendly, whereas Blockbuster which had a larger catalogue of movies and games to rent if they had looked at the data of their sales and looked into the direction of the internet and consumer trends, they would have survived. The Oakland Athletics and Leicester City scouted different players in unfavorable situations that would have been overlooked by many top teams in their respective leagues and trusted in the data rather than the aesthetic. They needed players who could do their job and put up the numbers needed that suggested that they should be winning more games than they should be losing if they joined. The larger teams around them could have done this with the vast resources available to them, the galore of their histories to attract all of these players… but they didn’t. They didn’t look at the data that told them there was a cheap player in a lower league, released from a good team, consistent, and with one last good season in them to help.
Instead, they signed these players later in large multi-million pound/dollar deals.
Investment funds, Scientific Research groups, Social media companies, Sports teams, Academic Institutions, and many more have large volumes of data available to them. They have spent millions and in some cases billions in this pursuit of knowledge.
"However, we live in a world where knowledge is a commodity and Wisdom is an illiquid market...
Queue Vectorspace AI."
From my understanding of Vectorspace, it is a company that builds data sets and tables showing the correlation between different variables for machine learning systems. In turn, this enhances natural language processing (NLP), the process for machines to analyse human language i.e., identify keywords or phrases. They also build these data sets and tables to aid with natural language understanding (NLU) the process for machines to understand the context behind the language used.
Basically, to put into short they are liquidity for wisdom, they provide ease and understanding for knowledge which would usually be overlooked.
To understand the full gravity and scope of what this company is trying to solve, I must point you briefly to the current landscape of the world around us. In my previous blog, I wanted the reader to acknowledge that humanity is going through a technological shift. We are moving into the digital age, the 4th Revolution. How we use the data around us is about to change dramatically, data will be traded on the open market via NFTs (non-fungible tokens), our energy will likely in the near future be traded on a local market by members of our own community rather than just large energy companies, (for more information I suggest looking into energy web token). In the future we will likely be able to sell our data for a price to these social media companies to use, we already have solutions built to compensate us for having advertisements thrown at us 24/7 e.g., Brave browser.
As a scientist myself, when I did my thesis for my degree I did a meta-analysis (an analysis on large amounts of data gathered from multiple related studies) comparing two chemotherapy drugs, Paclitaxel and Docetaxel in their treatment for Metastatic Breast Cancer (MCB). In short, there was no significant difference between the two in their effectiveness to treat MCB. The data I gained from that study and the conclusion made me wonder how many drugs were being developed unnecessarily that didn’t solve a problem? Were these drugs simply being developed for the sake of being developed? Was it all about the money? A drawback from the whole analysis for me was that there was a lot of wasted data that didn't make the cut, but having stumbled across Vectorspace, I wonder if at the time of the analysis if I had their technology available to me, would I have been able to find further hidden relationships in the data I had extracted from the numerous studies?
I will not be the only one who has thought this. Vectorspace, having partnered with PubMed – a database for scientific literature and the national library of medicine will have thousands of customers from the scientific community and hundreds of large scientific and academic institutions enquiring into their service throughout the next decade.
Vectorspace, besides working in the bioscience area, aims to provide datasets for financial institutions such as the S&P 500, and I suspect it won’t just be for them but many other large exchange groups. The question then becomes how much longer before other sectors begin to enquire, such as sports teams and other businesses.
Advancements in artificial intelligence and machine learning are already being seen notably by companies such as Tesla and Google with their self-driving cars. Data-driven networks such as Facebook and Instagram will further look into artificial intelligence to enhance the quality of the data and advertisements they push to us consumers to enhance their own revenues.
If you’re not looking into Vectorspace the company or the space they are in then someone else will… for what is the cost of understanding in a world where knowledge is a common commodity and easy to obtain, but wisdom is an illiquid market?