If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.
Data Science refers to the multi-disciplinary field that extracts knowledge and insights from data using processes, algorithms, and systems based in the scientific method. Data science is a melting pot of statistics, machine learning, and data analysis, using each to their strengths to understand and conceptualize real-world phenomena. Referenced as early as 1960, the term "data science" didn't really come into its own until 1990's. As technology became more powerful, and the access to information became more widespread, the applications of data science proliferated. Turing award winner Jim Gray refers to data science as the "fourth paradigm" of science, along with empirical, theoretical, and computational.
Be careful which skills you put on a pedestal, since the effects of unwise choices can be devastating. In addition to mismanaged teams and unnecessary hires, you'll see the real heroes quitting or re-educating themselves to fit your incentives du jour. A prime example of this phenomenon is in analytics. The top trophy hire in data science is elusive, and it's no surprise: "full-stack" data scientist means mastery of machine learning, statistics, and analytics. When teams can't get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Today's fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning darlings of the hiring circuit.
The first neural network you want to build using squaring of numbers. Every time you want to learn about NNs or data science or AI, you search through google, you go through Reddit, get some GitHub codes. There is MNIST dataset, GANs, convolution layers, everywhere. Everybody is talking about neural networks. You pick up your laptop, run the code, Voila! it works.
The amazing capabilities of AI, ML, and Predictive Analytics will lead to the transformation of various insurance processes beyond recognition. FREMONT, CA: Technology is transforming various aspects of the insurance industry. Innovative digital tools are streamlining insurance processes. Data science is one of the many areas in insurance which is impacted by technological advancements to a great extent. Data science is fueled by artificial intelligence (AI) and predictive analytics capabilities and offers insurance companies with actionable, concrete insights into a wide range of insurance processes.
I will be sharing my perspective on which is actually more sought after in the current industry. Let me ask you one question. If you were the tech lead of data science, and there already has a lot of Ph.D. people working for you, at the same time, you would like to expand your team. You have two candidates in mind, one is better in coding and one is better in math concept, which candidate will you prefer? There is no right or wrong answer to this question, but from what I observed, usually, they will prefer the ones who have better skills in coding.
Many people fear that the rise of Artificial Intelligence (AI) in any industry is going to eliminate their jobs. In healthcare, for instance, AI is already making a big splash in radiology. But rather than eliminating the job of a radiologist, AI is transforming what the role is. Not only that, and perhaps most importantly, AI has the potential to enable better patient care and lower costs in the end. Since the first X-ray was taken in 1895, a lot has changed.
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could not be obtained from any of the constituent learning algorithms alone. The idea of combining multiple algorithms was first developed by computer scientist and Professor Michael Kerns, who was wondering whether "weakly learnability is equivalent to strong learnability ". The goal was turning a weak algorithm, barely better than random guessing, into a strong learning algorithm. It turned out that, if we ask the weak algorithm to create a whole bunch of classifiers (all weak for definition), and then combine them all, what may figure out is a stronger classifier. AdaBoost, which stays for'Adaptive Boosting', is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance.
Data science is a required practice for organizations accelerating their journeys to AI. Businesses are keen on hiring the right talent, acquiring the right tools and evolving the discipline. Solving the lack of data scientists' problems requires investment in our employees in terms of time and training. We can't expect these people to just keep on learning for a year before they can be productive. We need to reach a stage where people know enough to start contributing immediately while continuing to improve their skills. As far as the second problem is concerned, taking too much time getting to a usable and tuned model, we need tools to help us optimize our data scientists' productivity.