"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.
I recently had the privilege of participating on a panel with several AI/Machine Learning experts. There were many great questions, but most were related to how to most effectively establish an AI/Machine Learning (AI/ML) in a large organization. This gave me an opportunity to reflect on my own experiences helping large enterprise accelerate their AI/Machine Learning journey, and, more specifically, assess what worked, and perhaps just as importantly, what did not work. I have condensed these into a few simple "lessons learned" that hopefully will be useful to you on your organization's AI/ML journey. In my experience, your models will never be perfect.
Now we are moving into the world of'edge computing', in which data is processed close to its source, cutting out the need for it to be sent to the cloud. But computing isn't the only thing taking place on'the edge' – now, AI is being brought to the source of the data as well, allowing'Edge AI' to bring about new standards of speed and intelligence. So, what is Edge AI, what kinds of benefits will it offer, and how will it empower solutions going forward? Currently, the heavy computing capacity required to run deep learning models necessitates that the majority of AI processes be carried out in the cloud. However, running AI in the cloud has its disadvantages, including the fact that it requires an internet connection, and that performance can be impacted by bandwidth and latency limitations.
It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on servers/IoT device performances are collected every hour for each of thousands of servers in order to identify servers/devices that are behaving unusually. Python library tsfeature helps to compute a vector of features on each time series, measuring different characteristic-features of the series. The features may include lag correlation, the strength of seasonality, spectral entropy, etc.
When you think of artificial intelligence (AI), what do you envision? For decades, pop culture and science fiction have conspired depictions comprising inspired images of machine-ruled futures and robots accomplishing incredible tasks for human beings. The pictures painted by them are primarily futuristic and incredibly independent. That lays a powerful impression on people. So much so that it can be overwhelming and misleading at times.
In many projects I carried out, companies, despite having fantastic AI business ideas, display a tendency to slowly become frustrated when they realize that they do not have enough data… However, solutions do exist! The purpose of this article is to briefly introduce you to some of them (the ones that are proven effective in my practice) rather than to list all existing solutions. The problem of data scarcity is very important since data are at the core of any AI project. The size of a dataset is often responsible for poor performances in ML projects. Most of the time, data related issues are the main reason why great AI projects cannot be accomplished.
Let's say you're looking to buy a new PC from an online store (and you're most interested in how much RAM it has) and you see on their first page some PCs with 4GB at $100, then some with 16 GB at $1000. So, you estimate in your head that given the prices you saw so far, a PC with 8 GB RAM should be around $400. This will fit your budget and decide to buy one such PC with 8 GB RAM. This kind of estimations can happen almost automatically in your head without knowing it's called linear regression and without explicitly computing a regression equation in your head (in our case: y 75x – 200). So, what is linear regression? Linear regression is just the process of estimating an unknown quantity based on some known ones (this is the regression part) with the condition that the unknown quantity can be obtained from the known ones by using only 2 operations: scalar multiplication and addition (this is the linear part).
Tesla plans to offer machine-learning training as a web service with its new'Dojo' supercomputer, according to new comments from CEO Elon Musk. Project "Dojo" was first announced by Musk at Tesla's Autonomy Day last year: We do have a major program at Tesla which we don't have enough time to talk about today called "Dojo." The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do unsupervised massive training of vast amounts of video with the Dojo program -- or Dojo computer. Dojo means "place of the Way" in Japanese and the term is often used for a place to practice meditation or martial arts. In this case, the Dojo supercomputer will be a place for Tesla to train its Full Self-Driving AI. Last month, Musk revealed that Tesla's Dojo supercomputer will be capable of an exaFLOP, one quintillion (1018) floating-point operations per second, or 1,000 petaFLOPS.
I know for sure that human behavior could be predicted with data science and machine learning. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys. In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I'll use a retail business as an example. Moreover, considering uncertainties related to the COVID-19 pandemic, I'll also describe how to enhance forecasting accuracy.
On 1st January 2019, we (Fabin Rasheed and I) had introduced to the world, a side project we've been working on for months. An artificial poet-artist, who doesn't physically exist in this world but writes a poem, draws an abstract art based on the poem and finally color the art based on emotion. We called "her" Auria Kathi -- an anagram for "AI Haiku Art". Auria has an artificial face along with her artificial poetry and art. Everything about Auria was built using artificial neural networks.
With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).