"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Over the past year, the sheer number of ransomware attacks have increased dramatically, with organizations of all stripes being affected: government entities, educational institutions, healthcare facilities, retailers, and even agricultural groups. While the bulk of the media attention has been on critical infrastructure and large organizations, attackers are not limiting themselves to just those types of victims. "That's really just the tip of the iceberg," says Max Heinemeyer, director of threat hunting at Darktrace. "We see not just big names being hit. It's basically any company where adversaries think they can pay the ransom. Anybody who's got money and running some kind of digital business is basically in the crosshairs."
Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?
To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death (Mark O'Connell). "Flesh is a dead format," writes Mark O'Connell in To Be a Machine, his new nonfiction book about the contemporary transhumanist movement. It's an alarming statement, but don't kill the messenger: As he's eager to explain early in the book, the author is not a transhumanist himself. Instead, he's used To Be a Machine as a vehicle to dive into this loosely knit movement, which he sums up as "a rebellion against human existence as it has been given." In other words, transhumanists believe that technology -- specifically, a direct interface between humans and machines -- is the only way our species can progress from its current, far-than-ideal state.
The Consumer Stocks Package is designed for investors and analysts who need predictions of the best performing stocks for the whole Consumer Industry. It includes 20 stocks with bullish and bearish signals. Package Name: Consumer Stocks Recommended Positions: Long Forecast Length: 1 Year (10/13/20 – 10/13/21) I Know First Average: 210.61% The algorithm correctly predicted 9 out of 10 the suggested trades for this 1 Year forecast. The top performing prediction from this package was GME with a return of 1459.83%.
Many deep learning models pick up objectives using the gradient-descent method. Gradient-descent optimization needs a big number of training samples for a model to converge. That creates it out of shape for few-shot learning. We train our models to learn to achieve a sure objective in generic deep learning models. However, humans train to learn any objective. There are different optimization methods that emphasize learn-to-learn mechanisms.
Putting yourself in a data science role when you've been given the amazing task of building this cutting-edge machine learning solution. You have the data and the motivation but don't know where to start. Is it clear in your mind or you have this rush in your chest but without exactly seeing the path and where to begin? My motivation here is simple: give you, in a straightforward way, where to start and also why each step is important. I remember when I started this journey into the data world, being a little bit crushed under the data science buzz words with the associated technics: it was like being in a storm on a little canoe.
This Commodities Package is designed for investors who need commodity recommendations to find the best performing commodities in the industry. Package Name: Commodities Recommended Positions: Long Forecast Length: 1 Month (9/12/21 – 10/12/21) I Know First Average: 12.32% In this 1 Month forecast for the Commodities Package, there were many high performing trades and the algorithm correctly predicted 10 out of 10 trades. The prediction with the highest return was BCOMCO1T, at 15.53%. BCOMCL2T and BCOMCL3T also performed well for this time horizon with returns of 15.17% and 14.54%, respectively. The package had an overall average return of 12.32% during the period.
At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.
Let's face it, we humans make a lot of bad decisions. And even when we are deeply aware that our decisions are hurting ourselves -- like destroying our environment or propagating inequality -- we seem collectively helpless to correct course. It is exasperating, like watching a car heading for a brick wall with a driver that seems unwilling or unable to turn the wheel. Ironically, as individuals, we are not nearly as dysfunctional, most of us turning the wheel as needed to navigate our daily lives. But when groups are involved, with many people grabbing the wheel at once, we often find ourselves in a fruitless stalemate headed for disaster, or worse, lurching off the road and into a ditch, seemingly just to spite ourselves.
Artificial Intelligence (AI) is a skill one can use for a successful career in any field. It is no longer restricted to the IT industry and professionals with non-technical backgrounds are also entering the field of AI through upskilling. Picture this: the experts' estimation about AI is that by 2030, the contribution of the AI market to the world's economy will be more than USD 15$ trillion. However, there is a huge shortage of skilled (aka certified) professionals in the field of AI. For those who wish to make their career in the field of AI, this is the right time.