You might have already heard that robots will one day take the world. This is a topic explored for almost 100 years now, and it is being heavily mentioned over the last 20 years with the evolution of computers and processors in general.
Cinema and news love to instill this fear on you. The list of movies showing this idea is long. From the 1927 Sci-fi German movie “Metropolis” to modern days and very famous Blade Runner, Terminator, Robocop, The Matrix, Ex-Machina and the name itself… AI – the movie, all of them and many others try to explore the dangers of robotics and technological progress.
If you search for machine learning or robotics online, one of the most popular messages shared by media companies is clearly: that machines will take your job (and kill you).
But let me give you some solace: that’s not going to happen. Not right now.
The way our processors (CPUs and GPUs) capacity to compute information is growing that’s likely to happen soon, and companies around the world have already started to take advantage of these changes. Soon you will be a Hommer Simpson going back home because Mr. Flint fired him and put more efficient computers to do your work.
Robots to get the bus and go work for me? Checked?
Robots to go out shopping? Checked!
Robots to cook. Checked!
Robots to entertain the kids? Checked!
Robots to think for me? Checked!
Robots to get drunk and high for me? Checked!
Money to pay the robots? Unchecked!
You’re pretty much unemployed by this time and your boss is very happy after all, you, the lazy bastard, is no longer working with him and sitting by his side is a beautiful bot, with big soft lips, blue eyes, small nose, and soft skin, filling the papers, giving him business advice, projecting gains, predicting errors and failures, checking if the machines are running, “firing” the lazy machines and hiring new ones, etc. Nothing was left to you if not lay on the sofa or go to the next bar and buy some beers to kill the time while watching babies and dogs’ videos on YouTube.
Oh, no! The alcoholic bot bought all the beers from this bar. Let me go to the next one.
Oh, no! The puppet bot of the owner of the bar hacked my bank account and found out that I’m broke. I can’t pay for these beers – “Please bot have some compassion!”.
Well, ok! I cannot get drunk but at least I still have a TV.
Oh, no! The internet provider bot cut my internet. ‘I told that stupid PayPal bot to process the payment but he didn’t do it’.
Well! At least I can go to bed and sleep with my bot companion, now that he jealously killed my wife.
The story above is likely impossible to happen. Human interactions will be alive for many years. But we’ll see a reduction in those interactions over the next decades.
Whether you are an employee or running a business, the evolution of AI will bring you big losses but if you pick the right side, teaching yourself and changing your processes, it will bring big gains.
In this article, we’ll explore the changes that machine learning, a subset of AI, is bringing to companies and jobs around the world. To do not make this article too long, we split it into two parts. In this part, we explore the changes machine learning is bringing to industries around the world. Anytime soon we’ll release the second part, talking about the changes it is bringing to jobs and professions around the world.
How machine learning is changing industries
Machine learning algorithms are changing many fields globally. Relying primarily on statistical data, machines (computers) are helping companies predict scenarios, create and improve existing products and improve customer relations in many fields.
We bring you a few examples of how machine learning is being used these days in each sector bellow.
Professionals in the healthcare industry are using machine learning models to diagnose patients and get real-time insights about the treatment and evolution of diseases.
Using machine learning, data is collected about the profile, history, environment and other key elements about patients to help doctors predict the accuracy of cancer diseases, identify patients that might benefit from a specific type of treatment or therapies.
In this sector, ML is also helping people better understand the benefits of healthcare services and find the nearest and least expensive providers.
The biggest goal of machine learning in the healthcare sector is to diagnose and suggest treatments to diseases as fast as possible.
eLearning platforms are using ML to personalize and recommend courses or topics based on your past interests. By doing so, it stops you from learning things that you have no interest and saves you time.
Using machine learning, startups are teaching people how to code, design, understand mathematics, biology, physics and other complex subjects in a fast and entertaining way.
Machine learning tools are speeding up the research process by helping scientists to run surveys, observe data, analyze and create research reports quickly.
It is also being used to match teachers with schools or students, based on shared goals and vision and plan the most efficient bus routes for students.
Transportation and logistics
Machine learning is being used in this field to control traffic. By monitoring and predicting the behavior of drivers, ML algorithms are being used to determine how long red or green street lights should be on.
ML is being used to determine the best routes and ways of distributing products, monitor warehouses, manage orders and reduce costs.
One of the biggest advancements of ML these days is found in this field. Google, Uber, Tesla, BMW, and many other companies are rushing to release to the market the first autonomous car. Many successful tests were already run, proving that soon we’ll be able to just sit down, relax, and let the car take us home.
10 companies using machine learning in transportation and logistics include the well-knowns, Uber, Google (Now Waymo), BMW, Tesla and startups such as LORI, TransFix, Convoy, Postmates, Overhaul and BlaBlaCar.
Machine learning is being used in the finance sector in many ways. Using data from past customers' behaviors, it is being used to detect and manage frauds, determine risks and provide financial advice.
Using risk assessment tools, machine learning is decentralizing the process of getting loans. By not relying on the traditional banking system, FinTech startups around the world are letting people quickly request and provide loans to each other, on lower taxes.
Machine learning is also being used by companies and private investors to determine patterns in the market to properly direct their investments, decide to buy, sell shares or make other substantial changes in their portfolio.
Marketing and Sales
ML is being used by companies to match their products to prospects profiles. Using CRM software, companies can determine which product, campaign or offer is the best for each client or prospect.
Using data collected from many social platforms and other sources, companies are using machine learning to target specific niches, predict customer lifetime and churn-rates.
The large amount of data collected about customers these days, allows companies to properly sort them in specific niches and determine the score of leads based on their interests, acquisition strategies, interaction with the website and other tools of the company.
Machine learning models help sales managers to forecast demands for products and services; schedule their time properly and develop promotional programs, discounts, and other price strategies.
There are many tools in the market today using machine learning to automate marketing and sales processes. They include: Hubspot, Salesforce Pardot, Oracle Eloqua, Keap (former Infusionsoft), XANT, Demand Base, 6sense, BounceX and Cogito.
This is the first field we think about when we talk about AI. The combination of robotics and AI is the path to build human-like machines, physically and psychologically.
Machine learning is being used in robotics to enhance the capabilities of industrial, consumer, military, medical and other types of robots.
This field takes advantage of artificial intelligence and machine learning to improve the capabilities of robots to recognize objects – even if they were not seen before; calculate the distance, interact and avoid objects or obstacles; and understand logical patterns.
Besides being used to increase the vision and motion capabilities of robots, machine learning is also being used in supervised or non-supervised learnings, to enhance grasping and imitation capabilities to increase robot efficiency when performing multiple functions and mimic human gestures.
Examples of 10 startups using machine learning in robotics: Boston Dynamics, AirRobotics, DJI, Exotec, Impossible Aerospace, Exyn Technologies, Bright Machines, Sarcos Robotics, BlueFin and AMP Robotics.
Machine learning is used to mitigate and manage security problems. From crime prediction tools to cybersecurity, machine learning models make sure the plans of companies and average people don’t end up in the hands of the wrong people or be destroyed.
Travelers and tourists can use tools based on machine learning to gather information about their destination. Companies such as Redcrow and PredPol allows you to monitor and predict threats coming from war, crimes or diseases.
Other companies, such as Deep Sentinel, use cameras and sensors to detect potential threats in your property, such as trespassing, home intrusion, vandalism, and package thefts and report it to local authorities in minutes.
While some companies focus on avoiding physical threats, others are looking to protect your data online.
AI-based cybersecurity firms use machine learning algorithms, generated by analyzing previous computer behaviors, to help companies to detect malicious activities faster and stop the attacks. Companies in this field have been helping companies to avoid ransomware attacks, cryptojacking and luckily one day, identify and avoid zero-day exploits.
10 examples of companies building machine learning algorithms to improve security are: Sentinel One, Darktrace, Sophos, Chronicle Security, Intelligo Group, Cylance (BlackBerry), Cynet, Google Nest, SimpliSafe, Netatmo and Wyze.
Machine learning is useful in agriculture to detect diseases caused by bacteria or viruses and determine the proper crops to harvest based on season, soil data, farmers' goal, and other criteria.
Companies in this field can also take advantage of automation and machine intelligence to control irrigation based on weather and soil data, and help farmers better manage their water.
This sector is taking more advantage of robots and drones to identify and fight weeds; remotely control the growth of crops and deliver pesticides when necessary and to pick fruits.