Do you know that, Artificial Intelligence & Machine Learning technologies were used in the “moon shot” project. “Moon Shot” project was a bold plan of MD Andersons Cancer Center. They wanted to build a system to diagnose and recommend treatment plans for certain forms of cancer. IBM Watsons Cognitive Systems was used for this project.
The results of the “moon shot” project were not visible. And the costs of the system were escalating. They had to put it on hold after burning $62 Million.
On one hand the company had to stop the “moon shot” project because of escalating costs without any visible results. On the other hand the company’s IT group was experimenting with Artificial Intelligence and Machine Learning technologies.
They were experimenting with using these technologies in a less ambitious projects. Such as making hotel and restaurant recommendations for patient families. Or determining which patients needed help in paying bills, and addressing staff IT problems.
The results of these projects were much promising.
With these projects new systems were built using Artificial Intelligence and Machine Learning Technologies. These systems have contributed to increased patient satisfaction. It improved financial performance. And the most visible result was a decline in time spent on tedious data entry by hospital’s care managers.
This shows that Artificial Intelligence, Machine Learning,
and Big Data have already become the technologies of the present. They are not a dream of some distant future. They are here to stay.
What does it mean for you, if you are a software engineer? Before I can answer that let us look at the history of Artificial Intelligence and Machine Learning.
The birth of Artificial Intelligence and Machine Learning.
During the Second World War, noted British computer scientist Alan Turing worked to crack the ‘Enigma’ code. This code was used by German forces to send messages securely. Alan Turing and his team created the Bombe machine that was used to decipher Enigma’s messages.
The Enigma and Bombe Machines laid the foundations for Machine Learning. According to Turing, a machine that could converse with humans without the humans knowing that it is a machine. Such a machine would win the “imitation game” and could be said to be “intelligent”.
In 1956, American computer scientist John McCarthy organized the Dartmouth Conference, at which the term ‘Artificial Intelligence’ was first adopted. Research centers popped up across the United States to explore the potential of AI.
Researchers Allen Newell and Herbert Simon were instrumental in promoting Artificial Intelligence. They thought of AI as a field of computer science that could transform the world
You can see that these technologies, Artificial Intelligence and Machine Learning, are as old as the computers themselves.
We surveyed 250 executives who are familiar with their companies use of cognitive technologies. 75% of these executives believe that AI will substantially transform their companies within three years.
HBR Article : Artificial Intelligence for Real World
by Thomas Davenport & Rajeev Ronanki
What does it mean for you, if you are a software engineer?
The message for all software engineers is very simple. Requirement for software engineers with knowledge of Artificial Intelligence, Machine Learning, and Big Data is increasing every day.
As per
Every top industry expert in this field feels the same. On one hand there is an explosion in the requirement for an AI and ML related jobs. But on the other hand there is a scarcity of talent available across the world.
Artificial Intelligence is one of the most important things humanity is working on. It is more profound than, I dunno know, electricity or fire.
Sundar Pichai – CEO, Google
Machine Learning and Artificial Intelligence will empower and improve. Every business. Even government organisation. And philanthropy. Basically there’s no institution in the world that cannot be improved with machine learning.
Jeff Bezos – CEO, Amazon
By 2030, roughly 70% of companies will have adopted at least one type of AI technology, up from 33% today.
McKinsey Sept 2018
Artificial Intelligence and Machine Learning have moved from the era of discovery to the era of implementation. For now, at least, the focus has shifted from research to real-world application. The current era of AI can be compared with the era of electricity. In that era humans learned to apply electricity to all the tasks in their life. They used it for lighting a room, cooking food, powering a train, and so on.
Because of the impact of these cognitive technologies are making in every aspect of our life. There is an increase in the number of jobs, across the world, for these technologies.
How do these jobs in Artificial Intelligence and Machine Learning impact salaries across the board?
Software Company, UiPath scanned through job listings in 15 industries from countries leading in AI technology. They found 30,000 jobs listed for software engineers, intelligence researcher, sales engineer and product managers.
As per their study, they also found that China topped with 12,000 jobs listings, followed by the US with 7,000. Many other countries are looking for AI related talent. They are Japan, the United Kingdom, India, Germany, France, Canada, Australia, and Poland.
Demand for Artificial Intelligence Talent is exploding. There was 100% increase in job postings between June 2015 to June 2018. This study was done by job search site Indeed on their own platform.
The salaries for jobs in AI and ML have also increased. Companies are ready to pay a much higher salaries to software engineers and researchers. This increase has happened because of the scarcity of talent available in these technologies.
In India the Top IT companies have announced doubling the salaries of engineers who acquire working knowledge of these technologies.
Watch this video below where I talk about the announcement made by TCS and followed by Infosys and other companies.
How you can take advantage of this technology to advance your software engineering career?
The easiest way you can take advantage of this change in the industry is to learn these cognitive technologies. You can invest some of your time, money and energy into learning these technologies.
But before you invest your time, money and energy into learning these technology you need to know one thing.
These technologies are not like programming languages you had learned over a weekend. They are more difficult and deep. You will need to spend a substantial amount of time and energy to master them. Only then you can use them to advance your career.
It does not matter how much time you spend on learning these technologies. Whether it takes a month or a quarter or even a year to learn them. Because these technologies are here to stay. The jobs in these technologies are going to rise exponentially.
So your investment in learning will be recovered as soon as you find your first job.
You even have a chance to double your income once you have a good command over these technologies. Either your existing company can give you a raise. Or you can find another company that has a requirement for AI and ML.
What are some of the best artificial intelligence and machine learning courses?
Here are some of the best courses to master these technologies. These courses will teach you Artificial Intelligence, Machine Learning and Data Science.
Machine Learning foundation Course
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
What you will learn ?
- Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
- Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
- Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)
The course will also draw from numerous case studies and applications. You will learn how to apply learning algorithms to build smart robots (perception, control). There is a module on text understanding (web search, anti-spam). And also computer vision, medical informatics, audio, database mining, and other areas.
You can Enroll in Machine Learning Course and learn the fundamentals of Machine Learning.
Deep Learning: Exploring the Frontiers of AI
Deep learning is one of the most highly sought after skills in AI Technology.
This course will help you learn the foundations of Deep Learning. You can understand how to build neural networks, and learn how to lead successful machine learning projects.
This course also teaches you about Convolutional Networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
You will work on case studies from healthcare, autonomous driving, sign language, reading, music generation, and natural language processing. Not only will you master theory but also see how it is applied in industry.
You can Enroll into Deep Learning Course and Master Deep Learning.
Machine Learning with TensorFlow on Google Cloud Platform Specialization
Learn how to write distributed machine learning models that scale in TensorFlow, scale out the training of those models and offer high-performance predictions.
Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insights to bear on the problem.
Finally, learn how to incorporate the right mix of parameters that yields accurate, generalised models and knowledge of the theory to solve specific types of ML Problems.
You will experiment with end-to-end ML, starting from building an ML focused strategy and progressing into model training optimization, and productionalization with hands on labs using Google Cloud Platform.
You can Enroll into Machine Learning with TensorFlow on Google Cloud Platform and Master Machine learning.
What are some of the best Data Science Courses?
Here I am going to show you some of the best courses to master Data Science Technologies.
Data Science Specialisation
This specialisation covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data.
As a student if you can complete this specialisation then you will have a portfolio demonstrating your mastery of the material.
What you will learn?
- Use R to clean, analyse and visualise data.
- Navigate the entire data science pipeline from data acquisition to publication.
- Use GitHub to manage data science projects
- Perform regression analysis, least squares and inference using regression models.
You can Enrol in this Data Science Specialisation and create a portfolio demonstrating your mastery in Data Science.
What next?
Whether you are a fresher with a computer science degree or you are an experienced software engineer, you can take advantage of these new age technologies to advance your career.
You can learn the technologies from some of the courses I have mentioned above or by any other way you know is possible.
Artificial Intelligence, Machine Learning and Big Data are here to stay. These technologies are going to shape our future. If you are a software engineer then you can become part of shaping that future.
Hope you take full advantage of these technologies and make a mark in the world with your creations using these technologies.
Moreover, share this article with your friends on your favourite social media platform because it will help them in their journey of learning these technologies.