The time has come to speak about artificial intelligence and machine learning, robots, in the present tense. They are no longer just around the corner. They’re here today!
Merrill Lynch, the investment arm of The Bank of America predicts the global market for AI and robots will approach $153 billion by 2020. Moreover, some industries will experience up to a 30% productivity increase by incorporating these technologies.
Fully functional artificial intelligence (AI) is closer than we might think. Working prototypes already exist. Computer engineers haven’t yet created a true AI — but their work in this area has already had a great impact on several industries.
AI vs. ML
Before looking at machine learning in various industries, we need to take a look at the difference between ML and AI.
ML and AI are nearly synonymous. Yet, there is still a distinction. Artificial intelligence is a computer program. It is able to perform what humans normally can, such as speech recognition, translation from one language to another, or decision making. These computer programs can take steps to accomplish certain objectives.
Machine learning is a form of AI where computer systems actually learn, develop, enhance themselves and “evolve” when introduced to new and/or additional data. There is no need to program the computer in a traditional sense. Machine learning models are based on human learning techniques.
Intelligent machines are actually able to differentiate between streams of new information using available knowledge while making logical connections, combining ideas, and following thought patterns just like humans do. As Jen-Hsun Huang, CEO of Nvidia put it: “You essentially have software writing software.”
Deep learning vs. ML
There is also a “deep learning” aspect to ML. Deep learning is one of many approaches to machine learning. Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.
Deep learning was inspired by the structure and function of the human brain, namely, by the interconnectedness of many neurons and their pathways in the brain. Artificial neural networks (ANNs) are algorithms that mimic the biological structure of the brain.
AI and ML have a great role to play in industrial operations optimization. You can use AI and ML to advance the efficiency and effectiveness of important industries.
A helping “iron hand” for the steel industry
For instance, steel manufacturing companies can greatly benefit from AI tools such as ML-based optimization, control systems, and sensors. AI has the great potential and capacity to implement different technologies. In the end, steel production can be done more efficiently and more profitably.
Let’s take a look at two areas where ML can be of use in steel manufacturing.
- Optimization of production: When it comes to steel manufacturing industries, there are always a few unplanned events. For instance, the molten steel can break and pour out of the mold during the casting process. This can slow down the production of steel and even endanger the lives of workers. This can be both dangerous and expensive. ML play an important role in predicting such occurrences and thus helping to minimize them.
- Predictive maintenance: Steel manufacturing companies schedule weekly maintenance check-ups. ML can assist in this procedure by predicting a particular machine required maintenance. So instead of the fixed weekly maintenance schedules, an as need-based maintenance plan can be implemented. This is vital for manufacturing companies who have a large quantity of on-site industrial machines.
Applications of ML in pharma and medicine
The healthcare sphere is sitting on the brink of a treasure trove. The more data one has in the realm of healthcare, the more successful it is. ML helps achieve a more precise decision-making process. It also enhances the efficiency of clinical research, trials and newer tools for physicians and insurers.
ML healthcare applications during the last three years have attracted the highest level of funding. ML and AI assist physicians by informing them of more precise diagnoses of their patients due to a more comprehensive pool of database information. Some unique scanners are equipped with special hardware and systems which help to find health problems quicker and more accurately.
New emerging ML applications in pharma and medicine are just a glimmer of a grand future in data, innovation and analysis.
1 — Disease identification/diagnosis
Disease identification and diagnosis is on the cutting edge of ML research in medicine. ML is actively being used for cancer treatment. According to a 2018 report on Medicines in Development for Cancer: 1,120 medicines and vaccines for cancer are currently in development by America’s biopharmaceutical companies, all of which are in clinical trials or awaiting review by the U.S. Food and Drug Administration (FDA).
It’s no surprise that large companies are some of the first to follow suit, particularly in such vital areas as cancer identification and treatment. For instance, in October 2016, IBM Watson Health announced IBM Watson Genomics, a synergetic initiative with Quest Diagnostics which purports to make progress in precision medicine. Their goal is to integrate synchronizing cognitive computing and genomic tumor sequencing.
2 — Robotic surgeries
Machine learning and AI are literally reshaping the healthcare industry. Doctors apply machine learning by conducting invasive surgeries with the help of robotics. They attest to safer and more efficient conditions of such operations. In fact, some surgeries are done by an “iron hand” without human direct control and achieve a greater level of precision.
The da Vinci robot is in the spotlight in the robotic surgery arena. The skilled hands of an “iron surgeon” are used to perform some surgeries in very tight spaces. This accomplished with less iron-hand trembling then if the procedures are done by human hand.
Presently, da Vinci-like robots greatly supplement the skill and expertise of a professional surgeon. In the future, ML might be used to allow da Vinci-like robots to master the art of surgeries from A to Z without heavily relying on human dexterity.
Machines have recently been trained to copy the painting of Van Gogh and Picasso. It’s not hard to imagine when machines could actually be “taught” to perform surgeries that are more precise and pointed than those done by a human hand.
The list of where ML is being implemented in pharma and medicine goes on and on (diagnosis in medical imaging, drug discovery, personalized medicine and etc.)
Business and marketing adopting ML
General business is also being impacted by the AI invasion. Of the 168 largest companies in the world, as many as 76% are using machine learning technologies to enhance their sales growth strategies, according to an MIT survey.
MarketMuse is an AI-powered research assistant that accelerates content creation and optimization so you can win more often in organic search. This is basically banking on AI as it also is moving toward helping determine more of your content marketing strategy.
AI and machine learning are used for determining the best topics to write about, and options to cover them more thoroughly. After that, your current content inventory is analyzed to create a comprehensive content plan custom-made for you and your website. In a nutshell, this tool keeps you timely and relevant by giving you some great tips!
We already take for granted precise recommendation engines used on Amazon, the New York Times, and Netflix. You are probably well aware of how smart these engines have become! The engines use certain learning algorithms. They examine your past activity and purchases to provide a fairly accurate prediction on what you might like to buy, read or watch next.
You’ve probably experienced how Google’s own RankBrain gets smarter and smarter with each Google search you typed. It’s no surprise. This “brain” is actually using AI and learning to better process search queries and return more relevant results.
You’re probably getting used to intelligent chatbots which are there to assist you online 24/7. They are super speedy in their feedback and they supply incredible data-backed insights and customer profiles very efficiently. Increased productivity. Higher profits. What’s not to like!
Import.io enables users to convert masses of data on websites into structured, machine-readable data with no coding required. And it is free!
We can go on and on. We could mention SailThru that applies machine learning to your email marketing. iPhone 7 uses ML for its super cool camera, which will take better pictures over time as it learns! Fraud detection is also possible in real time. This is possible due to the fact that more and more security systems are using ML with their big data machine learning algorithms.
There’s not enough time to describe how drastically ML has affected social media and continues to shape it. Whether it’s messaging apps or data science ML and AI is making its advance in continuing to revolutionize the digital world.
AI in the media and entertainment industry
After certain breakthroughs in ML, many smart products have made the leap from sci-fi movies to the home. Superhero Ironman’s virtual assistant JARVIS (Just A Rather Very Intelligent System) is echoed in smart assistants such as Alexa and Google Assistant. It may not detain criminals but it can do a range of practical chores via IoT household devices. NVIDIA uses VR technology to create a Holodeck similar to one in the sci-fi series Star Trek.
ML and AI technologies are being used for creating movies, enhancing visual design, post-production, and many other processes.
AI applications in the M&E industry exist mainly in four categories: marketing and advertising, service comprehension, search and classification, and experience innovation.
Let’s look at marketing and advertising. A machine learning algorithm trained with data such as text, stills, and video segments can extract language, objects, and concepts from its training resources and suggest marketing and advertising solutions to improve efficiency.
For example, the trailer for the horror movie “Morgan” was created by IBM’s AI Watson system. The AI system has been trained to analyze and classify 100 horror movies to learn what type of “horror moments” should appear in a typical movie trailer. It only took Watson 24 hours to compile a six-minute trailer to this movie. Human professionals would have likely needed weeks for such a clip production.
AI and ML are popping up everywhere. They are seen in education, transportation or in financial services, which could be an article on its own for the next blog. Machine learning systems continue to pave a new road for humanity. Machine learning influences entire industries and will continue to do so.
The prognosis is this: AI, machine learning, modern robotics and automation systems will most likely impact every industry in the near future — from retail and customer service to public transportation. In this respect, the future is certainly bright. As the saying goes: the sky’s the limit! Come and see how ML could enhance your business or industry with help of our developers’ team!