Article | April 2, 2020
The outbreak of coronavirus has taken many countries under its hood. Most of them are suffering from economic loss and a higher mortality rate. Amid this, governments are in a great dilemma how to handle the circumstances around the falling economy and upsurging coronavirus infections. In order to get better hold onto situations across their countries, they are moving towards innovative technology adoption. Out of all the new-age technologies, big data and data analytics can serve with a great opportunity, where governments across various nations can understand the outbreak analytics.
Article | April 2, 2020
DevOps will provide over-the-air (OTA), seamless software updates which would allow important and immediate updates without affecting the car’s capabilities through Liquid Software liquid software. OTA updates will enable automakers to fix engine and automotive malfunctions, as well as implement safety standards directly into the program. Tesla is one of the pioneers of over-the-air updates but while its’ cars are off. In total, Tesla’s updates are usually about 30 minutes. Since 2012, hundreds of OTA updates have been sent out by the company to adjust things like speed limit settings, acceleration, battery issues, and even braking distance. Most car manufacturers are behind when it comes to over-the-air software updates.
Article | April 2, 2020
Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts.
What Machine Learning is
So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on.
How Machine Learning is different from Statistics
It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it.
Machine Learning’s role in data science work
Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using.
Machine Learning and AI
Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes.
Learning more about the topic
Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!
Article | April 2, 2020
Today when we look around, we see how technology has revolutionized our world. It has created amazing elements and resources, putting useful intelligence at our fingertips. With all of these revolutions, technology has also made our lives easier, faster, digital and fun. Perhaps at a point when we are talking about technology, Machine learning and artificial intelligence are increasingly popular buzzwords used in modern terms.Machine Learning has proven to be one of the game changer technological advancements of the past decade. In the increasingly competitive corporate world, Machine learning is enabling companies to fast-track digital transformation and move into an age of automation. Some might even argue that AI/ML is required to stay relevant in some verticals, such as digital payments and fraud detection in banking or product recommendations.To understand what machine learning is, it is important to know the concepts of artificial intelligence (AI). It is defined as a program that exhibits cognitive ability similar to that of a human being. Making computers think like humans and solve problems the way we do is one of the main tenets of artificial intelligence.