HOW IS YOUR COMPANY MANGING ITS AI & ML INITIATIVES.

What is AI?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 

How Artificial Intelligence(AI)works?

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using it’s computational prowess to surpass what we are capable of. 

To understand How Aritificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry.

• Machine Learning : ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns, analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data, saves a human time for businesses and helps them make a better decision.
• Deep Learning : Deep Learning ia an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
• Neural Networks : Neural Networks work on the similar principles as of Human Neural cells. They are a series of algorithms that captures the relationship between various underying variabes and processes the data as a human brain does.
• Natural Language Processingc: NLP is a science of reading, understanding, interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
• Computer Vision : Computer vision algorithms tries to understand an image by breaking down an image and studying different parts of the objects. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
• Cognitive Computing : Cognitive computing algorithms try to mimic a human brain by anaysing text/speech/images/objects in a manner that a human does and tries to give the desired output.

Artificial Intelligence can be built over a diverse set of components and will function as an amalgamation of:

• Philosophy
• Mathematics
• Economics
• Neuroscience
• Psychology
• Computer Engineering
• Control Theory and Cybernetics
• Linguistics

Advantages of Artificial Intelligence (AI)

• Reduction in human error
• Available 24×7
• Helps in repetitive work
• Digital assistance 
• Faster decisions
• Rational Decision Maker
• Medical applications
• Improves Security
• Efficient Communication

Disadvantages of Artificial Intelligence (AI)

• Cost overruns
• Dearth of talent
• Lack of practical products
• Lack of standards in software development
• Potential for misuse
• Highly dependent on machines
• Requires Supervision

Top Used Applications in Artificial Intelligence

1. Google’s AI-powered predictions (E.g.: Google Maps)
2. Ride-sharing applications (E.g.: Uber, Lyft)
3. AI Autopilot in Commercial Flights
4. Spam filters on E-mails
5. Plagiarism checkers and tools
6. Facial Recognition
7. Search recommendations
8. Voice-to-text features
9. Smart personal assistants (E.g.: Siri, Alexa)
10. Fraud protection and prevention.

Career Trends in Artificial Intelligence

Jobs in AI have been steadily increasing over the past few years and will continue growing at an accelerating rate. 57% of Indian companies are looking forward to hiring the right talent to match up the Market Sentiment. On average, there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles. Mumbai stays tall in the competition followed by Bangalore and Chennai. As per research, the demand for AI Jobs have increased but efficient workforce has not been keeping pace with it. As per WEF, 133 million jobs would be created in Artificial Intelligence by the year 2020.

What is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of Artificial Intelligence – the ability to learn from experience, rather than just instructions.

Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output. They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

Types of Machine Learning are

•  Supervised Learning
•  Unsupervised Learning
•  Semi-supervised learning
•  Reinforcement Learning

What is Supervised Learning?

Supervised Machine Learning applies what it has learnt based on past data, and applies it to produce the desired output. They are usually trained with a specific dataset based on which the algorithm would produce an inferred function. It uses this inferred function to predict the final output and delivers an approximation of it.

What is Unsupervised Learning?

With unsupervised learning, the training data is still provided but it would not be labelled. In this model, the algorithm uses the training data to make inferences based on the attributes of the training data by exploring the data to find any patterns or inferences. It forms its logic for describing these patterns and bases its output on this.

What is Semi-supervised Learning?

This is similar to the above two, with the only difference being that it uses a combination of both labelled and unlabelled data. This solves the problem of having to label large data sets – the programmer can just label and a small subset of the data and let the machine figure the rest out based on this. This method is usually used when labelling the data sets is not feasible, either due to large volumes of a lack of skilled resources to label it.

What is Reinforcement Learning?

Reinforcement learning is dependent on the algorithms environment. The algorithm learns by interacting with it the data sets it has access to, and through a trial and error process tries to discover ‘rewards’ and ‘penalties’ that are set by the programmer. The algorithm tends to move towards maximising these rewards, which in turn provide the desired output. It’s called reinforcement learning because the algorithm receives reinforcement that it is on the right path based on the rewards that it encounters. The reward feedback helps the system model its future behaviour.

Top 10 companies using Machine Learning

It's commonly believed that smaller startups are generally more dynamic and more innovative than larger, established market leaders. And it's a perception which is prevalent in all industries. 

But it's not always the case, since the creation of innovative products and services is often very expensive, and only a company with significant resources can realise these types of ambitions.

Much as we'd prefer not to use Apple as an example – because we can't afford their fancy-pants products – but its latest smartphones always pack a huge number of innovations, both in terms of hardware and software.

And while we are calling it the top 10 machine learning companies, it's probably more accurate to say it's about AI with an emphasis on ML.

And for those of us who might not know or remember, machine learning refers to a machine's ability to learn. Meaning, it doesn't have to be programmed for every single detail of a specific situation, just given parameters for its decision-making, which, together, are usually called machine learning models.

1.Google

2.IBM

3.Baidu

4.Microsoft

5.Twitter

6.Qubit

7.Intel

8.Apple

9.Salesforce

10.Paindrop

Machine Learning in Robotics – 5 Modern Applications

As the term “machine learning” has heated up, interest in “robotics” (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?

While only a portion of recent developments in robotics can be credited to developments and uses of machine learning, I’ve aimed to collect some of the more prominent applications together in this article, along with links and references.

Like many innovative technological fields today, robotics has and is being influenced and in some directions steered by machine learning technologies. According to a recent survey published by the Evans Data Corporation Global Development, machine learning and robotics is at the top of developers’ priorities for 2016, with 56.4 percent of participants stating that they’re building robotics apps and 24.7 percent of all developers indicating the use of machine learning in their projects.

5 Current Machine Learning Applications in Robotics

1 . Computer Vision

Though related, some would argue the correct term is machine vision or robot vision rather than computer vision, because “robots seeing” involves more than just computer algorithms; engineers and roboticists also have to account for camera hardware that allow robots to process physical data. Robot vision is very closely linked to machine vision, which can be given credit for the emergence of robot guidance and automatic inspection systems. The slight difference between the two may be in kinematics as applied to robot vision, which encompasses reference frame calibration and a robot’s ability to physically affect its environment.

2 .Imitation Learning

Imitation learning is closely related to observational learning, a behavior exhibited by infants and toddlers. Imitation learning is also an umbrella category for reinforcement learning, or the challenge of getting an agent to act in the world so as to maximize its rewards. Bayesian or probabilistic models are a common feature of this machine learning approach. The question of whether imitation learning could be used for humanoid-like robots was postulated as far back as 1999.

3. Self-Supervised Learning

Self-supervised learning approaches enable robots to generate their own training examples in order to improve performance; this includes using a priori training and data captured close range to interpret “long-range ambiguous sensor data.” It’s been incorporated into robots and optical devices that can detect and reject objects (dust and snow, for example); identify vegetables and obstacles in rough terrain; and in 3D-scene analysis and modeling vehicle dynamics.

4. Assistive and Medical Technologies

An assistive robot (according to Stanford’s David L. Jaffe) is a device that can sense, process sensory information, and perform actions that benefit people with disabilities and seniors (though smart assistive technologies also exist for the general population, such as driver assistance tools). Movement therapy robots provide a diagnostic or therapeutic benefit. Both of these are technologies that are largely (and unfortunately) still confined to the lab, as they’re still cost-prohibitive for most hospitals in the U.S. and abroad.

5. Multi-Agent Learning

Coordination and negotiation are key components of multi-agent learning, which involves machine learning-based robots (or agents – this technique has been widely applied to games) that are able to adapt to a shifting landscape of other robots/agents and find “equilibrium strategies.” Examples of multi-agent learning approaches include no-regret learning tools, which involve weighted algorithms that “boost” learning outcomes in multi-agent planning, and learning in market-based, distributed control systems.

Advantages of Machine learning

1. Simply identifies trends and patterns.

2. No human intervention required (automation)

3. Continuous Improvement

4. Handling multi-dimensional and multi-variety data

5. Wide Applications

Disadvantages of Machine Learning

1. Data Acquisition

2. Time and Resources

3. Interpretation of Results

4. High error-susceptibility

?What Are The Career Opportunities In Machine Learning?

Machine Learning, a branch of AI, is a great career choice if you are interested in delving into how algorithms and statistical models work. Some of the popular career opportunities in Machine Learning for fresh graduates are as follows:

• ML engineer
• Software engineer/developer
• Data Scientist
• AI engineer

If you have a background in analytics and know programming, you can apply for the senior positions. With experience and certification, you can progress in your career in Machine Learning to move into lead and managerial positions or into research.

Top Career Paths in Machine Learning

Alan Turing stated in 1947 that “What we want is a machine that can learn from experience.”

And that was the beginning of Machine Learning. In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to Indeed, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

So now that we have established that Machine Learning is the future, the question that arises is….“What exactly is Machine Learning?”

Well, Machine Learning involves the use of Artificial Intelligence to enable machines to learn a task from experience without programming them specifically about that task. (In short, Machines learn automatically without human hand holding!!!) This process starts with feeding them(not literally!) good quality data and then training the machines by building various machine learning models using the data and different algorithms. The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.

1. Machine Learning Engineer

A Machine Learning Engineer is an engineer (duh!) that runs various machine learning experiments using programming languages such as Python, Java, Scala, etc. with the appropriate machine learning libraries. Some of the major skills required for this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc.

2. Data Scientist

A Data Scientist uses advanced analytics technologies, including Machine Learning and Predictive Modeling to collect, analyze and interpret large amounts of data and produce actionable insights. These are then used to make business decisions by the company executives.

3. NLP Scientist

First, the question arises “What is NLP in NLP Scientist ?”

Well, NLP stands for Natural language processing and it involves giving machines the ability to understand human language. This means that machines can eventually talk with humans in our own language.

4. Business Intelligence Developer

A Business Intelligence Developer uses Data Analytics and Machine Learning to collect, analyze and interpret large amounts of data and produce actionable insights that can be used to make business decisions by the company executives.To do this efficiently, a Business Intelligence Developer requires knowledge of both relational and multidimensional databases along with programming languages such as SQL, Python, Scala, Perl, etc.

5. Human-Centered Machine Learning Designer

Human-Centered Machine Learning relates to Machine Learning algorithms that are centered around humans (as if that were not obvious from the title!!). An example of this is video rental services like Netflix that provide their viewers with movie choices based on their preferences to create a “smart” viewer experience.

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