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2019 Predictions for AI & Analytics

by | Jan 28, 2019 | Data Science

2018 has been where AI and analytics surprised everyone. A large amount of time spent on research and experimentation has given us a clearer picture of what AI can do. But in 2019, organizations and professionals are going to invest in how these innovations can be used for real-life value-creating applications. In this article, we discuss AI and Analytics trends in three areas

  • Data Safety
  • Explanable AI
  • Industry applications

Data Safety

It is clear that companies are realizing how valuable data can be and how it can drastically change the decision-making process, for better. This led companies to collect more and more data about consumers. Fun fact – Facebook even keeps track of where your mouse pointer moves on the screen. Fine movements of ours are being recorded and processed to make our experience better. But it’s not so easy for these companies.

Consider the Equifax data breach that happened not too long back. For those of you who do not know, Equifax is a credit reporting company. In our case, this means that it holds very sensitive information about citizens. More than 800 million of them. In the data breach, over personal data of 143 million Americans were compromised. Some of the information over which hackers were able to gain access to included name, address, and even social security number. The company’s security department said it  “was aware of this vulnerability at that time, and took efforts to identify and to patch any vulnerable systems.” Equifax admitted it was aware of the security flaw a full two months before the company says hackers first gained access to its data.

There are many such examples of data breaches. Consequently, people are becoming more and more aware of their digital footprints. Government and lawmakers are realizing that data is no more just bunch of files on a computer. It has the potential to do good and to do the worst. This leads to the question – How safe are we digitally? What measures are being taken by the companies to safeguard our privacy?

Analytics is seen as a potential way to tackle this problem. Tools like user analytics try to understand steps a hacker takes when he crawls through the security walls of any company. It tries to understand if any user activity is different from the usual. For example a sudden spike in money withdrawal from a particular bank account. Deep Learning, arguably the most advanced form of analytics available to use is used on similar lines. Like user analytics, it focuses on anomalous behavior. The advantage is that it does it at a very micro level and is faster at detecting any such hazard, which is very important to take preventive measures.

Explanable AI

Today we hear about deep learning as if it is Harry Potter’s, Elder Wand. Deep Learning has given some surprising results. Consider the following painting created by AI, which got sold for $432k at an auction.

Image Source: Christie’s website

In case you are wondering what kind of signature is at the bottom right, it is the objective function of the GAN (Generative Adversarial Network) that produced this art. Now we know why Math is often called ‘Art’.

But as awesome it may seem, deep learning is still considered as a black box method. Meaning, we are not perfectly clear on what’s going under the hood. Consider the use of Deep Learning in Cancer Detection. The algorithm, out of 100 cancer positives, might detect 90 of them. But wrongly predicting even 10% is fatal. Moreover, we don’t know why did the algorithm make those 10 errors. This puts a limitation on deep learning. In addition, company executives cannot trust major decisions of a company with some algorithm that might be accurate, but can’t explain why. This is where explanable AI comes into the picture. So that we can go from ‘may be’, ‘should be’ and ‘can be’ to ‘is’ or ‘isn’t’. Explanable AI is an active research area.

Industry Trends

It’s a cliche to say AI will transform every possible industry on the earth. So let’s skip to the part where we specifically talk about a few selected industries and the ways.

Logistic Industry

In this nation of 1.25 B, where people are increasingly going online to shop, logistic is something every e-commerce focuses on the most. By forecasting demand and analyzing shopping patterns, companies can optimize their logistics. Thanks to real-time GPS data, weather data, road maintenance data and fleet and personnel schedules integrated into a system looking at historical trends, the most optimised routes and time are selected for delivery. Warehouse automation is something that has reduced the mundane repetitive work and has made processes faster and seamless.

Education

Personalization or the ability of the algorithms to go to the micro level details and offer the best recommendation could be very well used in education. When you and I studied, we probably sat in a classroom where we had 50 classmates who. Now some of us understood what was being taught and some of us didn’t. It was impossible to teach each one of us in a way we could understand. Because there wasn’t any unique way. AI can open doors to this kind of personalization and enhance our learning experience with multiple folds.

Disaster Management

We all know how dreadful disasters are. Life and property get destroyed in seconds when n earthquake or Tsunami strikes. In other cases like famine, a farmer’s livelihood gets disturbed. What if analytics can be used to predict the next earthquake or predict what crop would get the best yield? Startups like Cropin technologies provide smart solutions to farmers.

In 2015, researchers at the Vrije University Institute for Environmental Studies in Amsterdam published a research paper exploring how analyzing Twitter activity can help to detect flood events. The researchers found that by combining data from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity, disaster relief organizations can “gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods.”

 

If you liked this article and are trying to make your mark in this industry, do check out the following article –

https://dimensionless.in/10-data-science-skills-to-land-your-dream-job-in-2019/

How to be an Artificial Intelligence (AI) Expert?

by | Oct 25, 2018 | Data Science, Deep Learning, Learn Data Science, machine learning

How to be an Artificial Intelligence (AI) Expert?

Introduction

Artificial Intelligence is growing at a rapid pace in the last decade. You have seen it all unfold before your eyes. From self-driving cars to Google Brain, artificial intelligence has been at the centre of these amazing huge-impact projects.

Artificial Intelligence (AI) made headlines recently when people started reporting that Alexa was laughing unexpectedly. Those news reports led to the usual jokes about computers taking over the world, but there’s nothing funny about considering AI as a career field. Just the fact that five out of six Americans use AI services in one form or another every day proves that this is a viable career option

Why AI?

Well, there can be many reasons for students selecting this as their career track or professionals changing their career track towards AI. Let us have a look at some of the points on discussing why AI!

  1. Interesting and Exciting
    AI offers applications in those domains which are challenging as well as exciting. Driverless cars, human behaviour prediction, chatbots etc are just a few examples, to begin with.
  2. High Demand and Value
    Lately, there has been a huge demand in the industry for the data scientists and AI specialists which has resulted in more jobs and higher value given at workplace
  3. Well Paid
    With high demand and loads of work to be done, this field is one of the well-paid career choices currently. In the era, when jobs were reducing and the market was saturating, AI has emerged as one of the most well-paid jobs

If you still have thoughts on why one should choose AI as their career then my answer will be as clear as the thought that “If you do not want AI to take your job, you have to take up AI”!

Level 0: Setting up the ground

If maths(too much) does not intimidate and furthermore you love to code, you can then only start looking at AI as your career. If you do enjoy optimizing algorithms and playing with maths or are passionate about it, Kudos! Level 0 is cleared and you are ready to start a career in AI.

Level 1: Treading into AI

At this stage, one should cover the basics first and when I say basics, it does not imply to get the knowledge of 4–5 concepts but indeed a lot of them(Quite a lot of them)

    1. Cover Linear Algebra, Statistics, and ProbabilityMath is the first and foremost thing you need to cover. Start from the basics of math covering vectors, matrices, and their transformations. Then proceed to understand dimensionality, statistics and different statistical tests like z-test, chi-square tests etc. After this, you should focus on the concepts of probability like Bayes Theorem etc.  Maths is the foundation step of understanding and building those complex AI algorithms which are making our life simpler!

 

  1. Select a programming language

    After learning and being profound in the basic maths, you need to select a programing language. I would rather suggest that you take up one or maximum two programming languages and understand it in depth. One can select from R, Python or even JAVA! Always remember, a programing language is just to make your life simpler and is not something which defines you. We can start with Python because it is abstract and provides a lot of libraries to work with. R is also evolving very fast so we can consider that too or else go with JAVA. (Only if we have a good CS background!)
  2. Understand data structures
    Try to understand the data structure i.e. how you can design a system for solving problems involving data. It will help you in designing a system which is accurate and optimized. AI is more about reaching an accurate and optimized result. Learn about the Stacks, linked lists, dictionaries and other data structures that your selected programing language has to offer
  3. Understand Regression in complete detail
    Well, this is one advice you will get from everyone. Regression is the basic implementation of maths which you have learned so far. It depicts how this knowledge can be used to make predictions in real-life applications. Having a strong grasp over regression will help you greatly in understanding the basics of machine learning. This will prepare you well for your AI career.
  4. Move on to understand different Machine Learning models and their working 
    After learning regression, one should get their hands dirty with other legacy machine learning algorithms like Decision Trees, SVM, KNN, Random Forests etc. You should implement them over different problems in day to day life. One should know the working math behind every algorithm. Well, this may initially be little tough, but once you get going everything will fall in its place. Aim to be a master in AI and not just any random practitioner!
  5. Understand the problems that machine learning solves
    You should understand the use cases of different machine learning algorithms. Focus on why a certain algorithm fits one case more than the other. Then only then you will be able to appreciate the mathematical concepts which help in making any algorithm more suitable to a particular business need or a use case. Machine learning is itself divided into 3 broad categories i.e. Supervised Learning, Unsupervised Learning, and Reinforcement Learning. One needs to be better than average in all the 3 cases before you can actually step into the world of Deep Learning!

 

Level 2: Moving deeper into AI

This is level 2 of your journey/struggle to be an AI specialist. At this level, we deal with moving into Deep Learning but only when you have mastered the legacy of machine learning!

  1. Understanding Neural Networks

    A neural network is a type of machine learning which models itself after the human brain. This creates an artificial neural network that via an algorithm allows the computer to learn by incorporating new data. At this stage, you need to start your deep learning by understanding neural networks in great detail. You need to understand how these networks are intelligent and make decisions. Neural nets are the backbone of AI and you need to learn it thoroughly!
  2. Unrolling the maths behind neural networks
    Neural networks are typically organized in layers. Layers are made up of a number of interconnected ‘nodes’ which contain an ‘activation function’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. The hidden layers then link to an ‘output layer’ where the answer is output. You need to learn about the maths which happens in the backend of it. Learn about weights, activation functions, loss reduction, backpropagation, gradient descent approach etc. These are some of the basic mathematical keywords used in neural networks. Having a strong knowledge of them will enable you to design your own networks. You will also actually understand from where and how neural network borrows its intelligence! It’s all maths mate.. all maths!
  3. Mastering different types of neural networks
    As we did in ML, that we learned regression first and then moved onto the other ML algos, same is the case here. Since you have learned all about basic neural networks, you are ready to explore the different types of neural networks which are suited for different use cases. Underlying maths may remain the same, the difference may lie in few modifications here and there and pre-processing of the data. Different types of Neural nets include Multilayer perceptrons, Recurrent Neural Nets, Convolutional Neural Nets, LSTMS etc
  4. Understanding AI in different domains like NLP and Intelligent Systems
    With knowledge of different neural networks, you are now better equipped to master the application of these networks to different applications in Business. You may need to build a driverless car module or a human-like chatbot or even an intelligent system which can interact with its surrounding and self-learn to carry out tasks. Different use cases require different approaches and different knowledge. Surely you can not master every field in AI as it is a very large field indeed hence I will suggest you pick up a single field in AI say Natural Language processing and work on getting the depth in that field. Once your knowledge has a good depth, then only you should think of expanding your knowledge across different domains
  5. Getting familiar with the basics of Big Data
    Although, acquiring the knowledge of Big Data is not a mandatory task but I will suggest you equip yourself with basics of Big Data because all your AI systems will be handling Big Data only and it will be a good plus to have basics of Big Data knowledge as it will help you in making more optimized and realistic algorithms

Level 3: Mastering AI

This is the final stage where you have to go all guns blazing and is the point where you need to learn less but apply more whatever you have learned till now!

  1. Mastering Optimisation Techniques
    Level 1 and 2 focus on achieving accuracy in your work but now we have to talk about optimizing it. Deep learning algorithms consume a lot of resources of the system and you need to optimize every part of it. Optimization algorithms help us to minimize (or maximize) an Objective function (another name for Error function) E(x) which is simply a mathematical function dependent on the Model’s internal learnable parameters. The internal parameters of a Model play a very important role in efficiently and effectively training a Model and produce accurate results. This is why we use various Optimization strategies and algorithms to update and calculate appropriate and optimum values of such model’s parameters which influence our Model’s learning process and the output of a Model.
  2. Taking part in competitions
    You should actually take part in hackathons and data science competitions on kaggle as it will enhance your knowledge more and will give you more opportunities to implement your knowledge
  3. Publishing and Reading lot of Research Papers
    Research — Implement — Innovate — Test. Keep repeating this cycle by reading on a lot of research papers related to AI. This will help you in understanding how you can just not be a practitioner but be an thrive to be an innovator. AI is still nascent and needs masters who can innovate and bring revolution to this field
  4. Tweaking maths to roll out your own algorithms
    Innovation needs a lot of research and knowledge. This is the final place where you want yourself to be to actually fiddle with the maths which powers this entire AI. Once you are able to master this art, you will be one step away in bringing a revolution!

Conclusion

Mastering AI is not something one can achieve in a short time. AI requires hard work, persistence, consistency, patience and a lot of knowledge indeed! It may be one of the hottest jobs in the industry currently. Being a practitioner or enthusiast in AI is not difficult but if you are looking at the being a master at this, one has to be as good as those who created it! It takes years and skill to be a master at anything and same is the case with AI. If you are motivated, nothing can stop you in this entire world. ( Not even an AI :P)