Machines are gaining in capacity to learn on their own at an exponential rate, some parts of the tech being more developed than others. When we say the near-future of machine learning, we mean the future not later than 2022 + a decade and a half.
For now, machine learning services are mostly being used by businesses and tech giants. But we could see it make its way into day-to-day lives within this decade.
In the near future, Machine learning will evolve very quickly as these computer programs learn how to process our complex languages, personas, and needs effectively and efficiently.
- 1 The “very present” of Machine learning
- 2 The near future of machine learning
- 2.1 1) Domination in driving
- 2.2 2) Machine learning in our day-to-day lives
- 2.3 3) Medicine and health care will be revolutionized by Machine learning in a big way
- 2.4 4) Physical Robotics
- 2.5 5) Machine learning in Virtual Reality
- 2.6 6) Expert advice
- 2.7 7) Gene-editing
- 2.8 8) Cyber security
- 2.9 9) Help Business
- 2.10 10) Person-to-person (P2P) communication
- 2.11 11) AI will be able to predict the future
- 2.12 12) Precognition and sixth sense
- 2.13 13) Read minds up to an extent
- 3 Capitalization in Futuristic Machine learning Technologies
- 4 Possible changes that machine learning is going to make include:
- 5 Let’s conclude
The “very present” of Machine learning
Machine learning is becoming more and more prevalent in our daily lives. As per a 2020’s report by Fortune Business Insights, titled, “Machine Learning Market Size,…and Regional Forecast, 2020-2027″, the value of this market was USD 8.43 billion in 2019 and is likely to exhibit a CAGR of 39.2% to reach USD 117.19 billion by the end of 2027.
The main application of machine learning is found in the business world. These applications deal with, among other things, problems related to fraud detection, customer service, marketing, forecasting, recommendation engine, and more.
Google is actively investing in the field with its LaMDA AI. The intent is to give Google’s systems the ability to engage in human-like open-ended dialogue with users.
The Washington Post broke a story of a Google engineer, Blake Lemoine, claiming that one of Google’s advanced chatbots LaMDA (Language Model for Dialogue Applications) was sentient.
Conversation between Blake Lemoine and LaMDA:
In recent days, Lemoine also disclosed that LaMDA asked him to find an attorney for itself. According to Lemoine, he invited an attorney in to have a conversation with LaMDA and LaMDA chose to retain his services.
Likewise, Microsoft’s Azure has intelligent computer programs known as services, which is Microsoft’s public cloud computing platform. It provides a range of cloud services, including computing, analytics, storage, and networking.
The Azure platform aims to help businesses manage challenges and meet their organizational goals. It offers tools that support all industries. including e-commerce, finance, and a variety of Fortune 500 companies. The platform is compatible with open-source technologies. It provides users with the flexibility to use their preferred tools and technologies. In addition, Azure offers 4 different forms of cloud computing: infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), and serverless.
Facebook(Meta) produces the estimated action rate and ad quality score used in the total value equation using machine learning. It analyses photos, stories, and videos that users have previously interacted with before making some preliminary recommendations.
In it, we calculate the relevance score based on the positive and negative feedback we expect an ad to receive from its target audience. The more positive interactions we expect an ad to receive, the higher the ad’s relevance score will be.
In the present context, many use machine learning for personalized user experience. But it’s not yet available for the users (at least not in its best possible form).
The near future of machine learning
Now leave everything aside and dive into the possibilities of machine learning in our near future. This one matters the most for us.
We cannot keep the focus on the tech giants and the possibilities of machine learning only for them. It’s equally our future, too.
1) Domination in driving
Getting an autonomous vehicle today is as easy as parking your car in an open space and pressing the “Go” button. There is nothing to drive and no steering wheels are necessary! Soon, we’ll find riding on a vehicle without a driver as a common thing to do. And this could have happened by 2027.
By using object detection and object classification algorithms, driverless cars can identify objects, interpret situations, and make decisions. They accomplish this by detecting, classifying, and interpreting objects.
Cornell University researchers recently developed a method to assist autonomous vehicles in creating literal “memories” of previous experiences and using them for reference in future navigation,. This would help particularly during adverse weather conditions when the car cannot safely rely on its sensors.
We can expect these advances in the field of self-driving cars within the next decade:
a. It will become common for the general public
As of now, the cost of an autonomous vehicle is prohibitive. But as with any other new technology, the price will go down as the technology develops.
b. The cars are going to be more and more efficient
Sensors will enable vehicles to better position themselves and avoid accidents as well as eventually detect hazards in weather conditions that would otherwise cause accidents.
c. Maybe live inside a self-driving car?
It will be better for those who don’t want to buy an expensive house.
d. No more crashes
Since autonomous vehicles are going to drive themselves, they will also be not subject to human error. The more autonomous cars are around, the less likely accidents will be.
2) Machine learning in our day-to-day lives
The future is going to be more of a combo of AI and ML. This combo would significantly change the way we live our day-to-day lives.
Internet will be partially replaced by internet glasses in the next decade. This will be because of high-definition video streaming and will make use of machine learning to track and analyze objects that are being played or streamed. This data can then be used for automating tasks.
AI bots, which are intelligent chatbots, are going to interact with us within this decade. We’ll communicate with them and make judgments on our experience, which they could learn from. The tech will more than likely go silent if we don’t interact with it. This is the level of intelligence these bots work on now:
a. Speech recognition
Combing speech recognition and ML algorithms, AI systems can now listen to human speech better than ever before.
b. Understanding language
AI systems have started to understand the nuances of human behavior in order to provide better services.
c. An email recommending restaurants based on past experience
Most chatbots use machine learning algorithms to detect what the user wants and respond to it using basic rules and logic. The better we are able to communicate with them, the more we could teach them about our needs and wants.
3) Medicine and health care will be revolutionized by Machine learning in a big way
There is a lot of difference between a human brain and AI machines. It is our emotional quotient, understanding, and overall human experience that machines lack. On the other hand, computers do have some advantages over us:
a. Huge memory
Computer memory is unlimited and works more efficiently than ours. A computer could store every single medical article ever written and recall it in a fraction of a second.
b. Fast speed of processing data
A computer is easily capable of processing petabytes of data which would take even the fastest doctor days if not weeks to read and analyze on their own.
c. Intelligent cloud
Computers are easily automated by data that is available in the cloud. They are also able to access large amounts of data that humans could not access in a short time span and then make use of it for their decision-making process.
It is quite possible and likely that over the next decade, AIs will surpass us in some medical tasks, but what will become of doctoring?
4) Physical Robotics
In the near future, machine learning will be used not just to make software better, but also for making hardware more capable. One example is that of the “wearable” devices – intelligent devices that can be worn by humans and respond to their commands without an external power source.
The future of physical robotics is already here! Here are some examples.
a. Just as it looks like a human body, arm & hand movements can be mimicked by robots with increasing precision and accuracy. The next step will be gaining the same senses we have in order to more accurately mimic the human way of interacting.
b. Robots that can operate machinery, do work around the house, and interact with us will become commonplace.
c. Automated assistants that function through voice commands to perform tasks on our behalf will become very much a part of our lifestyle as well creating more efficiency in various areas such as household management and even our daily lives.
d. Powered exoskeletons that can assist the physically challenged will become prevalent.
5) Machine learning in Virtual Reality
Machine Learning has the potential to skyrocket the current level of Virtual Reality. The combo of “immersion” of Virtual Reality and Machine Learning’s ability to make sense of complex data and pattern recognition will significantly improve VR.
A good example is a Virtual Reality for medical training. Educators, as well as trainees, can use VR to safely explore the environment and deliberately practice various procedures – before using them in real life. This way, both are protected from all sorts of hazards that would come with working in an actual operating room or other scenarios where a mistake could be costly.
Machine learning can help Virtual Reality like this:
a. Detecting bodies
There are several ways in which machine learning can help VR. Machine learning has the ability to automatically detect bodies with motion, in order to avoid collisions -without having to constantly track every single one of them.
b. Intuition becomes more useful for VR games
It will be possible for players to have the same intuitive feeling that we have when playing a video game and get smarter and better at the game over time; something that is only possible with machine learning.
c. Learning more about human behavior
With blockchain technology, it will also be possible for VR companies to save on costs by working more efficiently, wasting fewer resources, and reducing environmental impacts.
6) Expert advice
It is really challenging to gather down 100 experts and ask for their opinion. I totally agree with it. Machine learning is going to make that easier. Instead of gathering 100 human experts, we could gather 100 algorithms, ask for their opinions and make the best decision.
I know this may sound far stretched. But I am including this as a near-future thing because I think we are moving towards this particular direction more than anything else. Machine learning is going to help with expert advice like this:
Gather down 1000 expert algorithms and ask them the best option. If 800 out of 1000 say purple, go with purple. This is how machine learning will help with expert advice like surveys.
b. Make the “research” part easier
With expert ML algorithms, the research part will be easier while still getting the same results. For example, a researcher may not be an expert in any of the fields mentioned in their article but is able to determine which field is most likely to give them the best output.
The more difficult part would be to understand what the “best” option is and that’s where machine learning can come in for us.
Maybe we will see many experts make it easier for us by using machine learning to analyze their work and make it easier for others to do the same…Maybe machine learning algorithms themselves are the “experts” here…
Gene-editing promises to create a new era in medicine and they are likely going to get better at it with time as well as experience.
Machine learning and AI will play a large role in the future of gene editing. Not only will they be used to improve existing technology and make it safer, but they will also help create new genome editing tools in the first place.
In order to take advantage of the potential gene editing can have for treating disease, more research is needed. Machine learning could be a source of such research because it can provide data in ways very similar to how we humans would key in data (keywords and phrases).
Gene-editing in the future is not going to be limited to curing diseases.
a. Gene-editing may also help to prevent diseases altogether
It may be possible to introduce beneficial changes into the genome of an embryo before it is born, in order to prevent disease or enhance the abilities of the person who grows up.
Researchers say if trial data continue to be so positive, the treatment could be approved as soon as 2023. According to Fyodor Urnov, Ph.D., IGI’s Director of Technology and Translation and a 20-year veteran of the sickle cell field, the bottom line, the progress of CRISPR/Vertex is a landmark in that it’s likely to generate the first approved CRISPR-based medicine.
With gene editing, many things related to cloning and designer babies become possible. There are things like designer babies who are free from disease or illness, as well as infertility.
b. Cloning and designer babies become a reality
Cloning becomes a possibility because it may also be possible to introduce new genetic material into the genome of an already born organism. Gene editing may make it possible to generate new types of cells, tissues, and organs for medical use with ease, becoming a revolution in the medical world.
The existence of the CRISPR baby project was uncovered by MIT Technology Review on the eve of an international genome-editing summit in Hong Kong, held in November 2018.
He Jiankui created shock waves in the year with the stunning claim that he’d altered the genetic makeup of IVF embryos and implanted them into a woman’s uterus, leading to the birth of twin girls – whom He called Lula and Nana.
Following international condemnation of the experiment, He Jiankui was placed under home arrest and then detained. In December 2019, He was convicted by a Chinese court, stating that the researcher had “deliberately violated” medical regulations and had “rashly applied gene editing technology to human assisted reproductive medicine.”
He’s experiment had received fierce criticism around the world and inside China. Scientists insisted that the use of genome editing served little medical purpose. And it could also have introduced errors into the girls’ genomes.
8) Cyber security
Cyber security is going to get better at a much faster rate. Machine learning is going to play a large role in this because it will be able to help us understand and predict cyber threats in much more effective ways. For example:
a. AI can keep an eye on user activity patterns
We are not able to understand what all these threats are, but we are able to create algorithms that can detect and predict cyber threats based on the user’s activity patterns. For instance:
The algorithm may be able to develop the ability to detect when a particular user’s actions aren’t within their normal range of behavior, since they have been hacked; something that would be more difficult for a human being.
b. AI can detect zero-day threats
It may be possible to create programs that can detect zero-day threats that a human being would not be able to see.
Often, a user will go into the cyber world unaware of the risks they’re taking, which is what makes them so vulnerable. The key is to catch cyber criminals as early as possible, which means we need intelligent AI that can analyze the increasing amount of cyber traffic and detect those causes for concern before it’s too late.
c. AI can block and delete infected files
It is possible to create neural algorithms that can find and identify infected files and block them, preventing them from being executed.
The same algorithms can also be designed so to delete all symptoms of the infection when it’s found.
This doesn’t destroy the infected file altogether, since some hackers may be able to restore these files from a backup. But it will prevent the risk of infection from spreading, and the risk of further hacks.
These algorithms will be an AI with a lot bigger part in future cyber security rather than just humans defending themselves against viruses in their systems by updating their anti-virus software every now and then…
9) Help Business
Machine Learning (ML) will play a huge role in helping businesses.
a. Business processes will be automated
Perhaps with the aid of AI, it may be possible to automate some business processes with some time rather than re-work them every time they are altered.
If we are able to automate some tasks that humans used to do, we should be able to save them much more time and energy for other things.
b. Business intelligence will improve
What if we can use artificial intelligence techniques to get a better understanding of our business? The possibilities that this type of data can offer are endless. As it turns out, the entire field may benefit from ML’s insights.
c. It will be a compulsion – not an option
Machine learning will become a compulsion for us.
For example, imagine being able to download Moody Blues onto your laptop and have it play all their songs from every album every time you log on. Traveling could become a lot more fun with this type of technology as it takes all the hassle out of traveling and finding exactly what you need at any given time.
10) Person-to-person (P2P) communication
The use of Machine learning-powered AI will allow us to interact with people much better. In the very near future, we will be able to use AI as a personal assistant. And the assistant would be capable to offer us great support.
It may also be possible for people to interact with each other much better than they do now.
Imagine being able to sit in front of your computer and having a conversation with someone who’s on the other side of the world – without any human intervention at all. This is the type of thing that will start happening in the very near future.
We are already seeing this technology being used today, but I think it’s still too far away. I predict people will start using this much more as time goes by.
ML in (P2P) communication:
a. AI will be able to help us understand the other person better
We are going to be able to use ML for things like voice recognition and speech-to-text. Then, we will be able to use this technology to help us get a very good understanding of what other people want/need. This will allow both sides of the conversation/conference call to actually understand one another as opposed to just trying their best.
b. AI can improve communication
If we are able to use AI for things like translation, speech recognition, etc., it should be possible for us to have more meaningful conversations with each other. This could lead the way back to ‘proper’ conversation again since many interactions today are limited by technology…
c. AI can help people understand and empathize with one another more easily
With language translation being much more accurate, and speech recognition becoming much better, it may be possible for us to become better at understanding the other person’s perspective.
It may also be possible for us to communicate without any language at all. Imagine this scenario:
You’re in a relationship and you have an argument with your partner. Now, instead of getting angry, you can use a computer program that listens to both sides of the conversation/argument. And then, it suggests some options for both of you to check out…
d. AI can help people find each other much easier
If we are able to use AI for things like pinpointing the exact location of someone using their cell phone or using facial recognition, it may be possible for us to interact with people a lot easier.
AI will also be able to help us use these technologies like remote control cars, helicopters, etc. They could be used to help us interact with people without actually being there…
11) AI will be able to predict the future
In the very near future, it’s quite possible that AI and Machine learning may be able to predict a possible version of the future.
While it’s certainly not possible to predict “the” future, ML could predict one with the most probability to occur.
This will show us which course of action to take, or provide a different route for us to follow that gives us the best outcome for our situation.
ML in Future Prediction
a. AI will be able to suggest the best possible course of action
If we can use ML for things like “fuzzy logic” calculation, it could be possible for us to predict a better outcome for our situation than we would have without any Computer intelligence.
In many cases, this type of prediction may come from our own intuition …
It’s just that machines are much faster in calculating the outcomes.
b. It will not be 100% accurate though
Although this type of prediction will take some time to become accurate, it won’t be 100% accurate.
But it would be a lot more accurate than what we have today.
Visual Analytics or Vision analytics is a new way of Visualization designed to make Data discovery much easier. In many cases, ML-based prediction can lead to Visual Analytics being used in different ways.
For example, we could use this technology for things like “analyzing” the possible outcomes of a given situation.
This may not be totally accurate, but as time goes by the accuracy will get better and better.
While people have been trying to develop such an application for quite some time now, not much has come out of it yet. I think that ML-Powered AI is going to be able to develop this type of application relatively soon.
12) Precognition and sixth sense
While we humans can not perceive many things with our naked senses, ML-Power Artificial Intelligence may be capable to use Machine learning-powered AI to predict the future.
This type of prediction may come from a number of different sources;
Some suggestions include:
a. The future sequence of events leading up to a big or serious incident (the big bomb, or the government takeover);
b. A person’s reaction or way of thinking before something happens (The twin towers falling down before it truly happened.);
c. The changes in the mental state towards something that hasn’t happened yet (The person who gets extremely angry before someone close to them dies.
d. The changes in posture, or expression before something happens.
e. The changes in facial expression before something happens (A person who has been anxious for weeks before their life is suddenly changed in an unexpected way.);
f. The sudden onset of a new mental state (An angry outburst against someone who’s stood there for 20 minutes);
g. The sudden change in behavior towards someone (Having just had a bad day, and then having a good day right after);
h. A person’s response to an outside stimulus or event.
13) Read minds up to an extent
As mentioned earlier, ML in the future may be able to predict the future.
In such a case, it may be possible for us to read minds to a certain extent.
Lawyers could use this as evidence in court against someone. For example, if a person is accused of murder and the computer mind reading system says that this person has “a strong urge to commit murder”, then this should count as evidence.
This type of prediction may also come from ML but with different sources.
For example, it could predict what you’re about to say:
When we are just starting out as friends, I will be saying quite a few things wrong. It’s noteworthy that I will be saying many things that my friend took offense to.
a. Now, by using a computer program powered by ML, my friend(ML) and I can communicate in a way that is much easier to understand.
b. I would say “What do you think I mean by this?”, then my friend would say what he thinks, and then I would say whether he’s right or wrong.
c. If it’s right, I would say “Yes, that’s what I mean”. And if it’s wrong, I would say “No. I don’t think that’s correct”. My friend would also react in the same way after finding my rights and wrongs.
d. Then we can use this technology to try and learn from each other.
My friend(ML)’s capability of reading the human mind will be one among many of its capabilities like forecasting an earthquake, predicting the future, etc.
Capitalization in Futuristic Machine learning Technologies
As we discussed above the possibilities of futuristic Machine Learning such as seeing a future, just can’t work if everybody sees the future. Capitalization is going to be a key factor in deciding how far future ML will go and how long it will stay.
Automatic capitalization is only very accurate if you teach the system to recognize certain words and already have a model of them. In the future, I believe we are going to see more and more software, able to automatically capitalize throughout the document.
Although Machine learning will be able to take over many aspects of our lives, it may not be able to completely replace people for certain tasks.
For example, an ML may be able to do things like “reading minds”, or suggesting the best course of action in general. But they will never be able to replace our actual feelings or emotional state.
This may probably be one of the “limits” of ML-Powered AI.
In some cases, this emotional aspect might be a good thing, such as in terms of safety.
An ML, who can read minds, may be able to predict some dangerous situation before it becomes a serious incident, but they will never be able to experience the same level of fear or anxiety as you do during that time.
Now, we can predict that machine learning is going to change many things in the world very soon.
Possible changes that machine learning is going to make include:
a) In our day-to-day lives
Machine learning is going to change some of the routine things in our daily lives.
For example, it is likely that machine learning may be able to help us live longer. If you are diagnosed with a disease at the age of 21 you may be able to survive with help of machine learning technology.
At that time, there will even be a possibility that by the year 2067, machines can predict medical conditions in advance and affect treatment options.
b) In work
Machine Learning is going to be able to eventually replace our current jobs.
For example, Machine Learning will likely perform far better than you. It may be able to do many things that we can’t even imagine today. Imagine a future where everything you ever said and did is recorded digitally. You could always query it and find out what you’ve missed.
c. In research and education
Machine Learning can help us achieve more and more, in the Medical field overall.
For example, we could use machine Learning to detect extremely rare diseases, or even to predict when a disease is likely to occur.
d. In Marketing and Advertising
Machine learning could help you predict anything in your mind at any time, which makes advertising and sales a little different from now or in the near future.
e. In security systems:
Nowadays there are many ways of securing our homes. We have electric fences, cameras around the house, and monitored alarms that go off if any stranger enters our property without permission.
- Creating consciousness in a machine vs creating it in a dead person
- Humans create AI — not long before AI creates ‘AAI’
- AI can help to reduce the biased points of view of humans
f. In space exploration
With the use of a machine learning rocket, our future may be in space.
g. In robotics
Robots will be able to feel the world around them through their sensors. They will learn from their experiences and act accordingly.
h. In technological development
This is a possibility that many people are worried about. In the near future, machines will control all our home appliances. These machines will be able to learn from their users.
i. In entertainment
Advancements in Machine learning technologies will revolutionize our toy and game industry and soon kids can get almost anything they desire in the virtual world with just the click of a button.
Now only a few are these, machine learning will have a strong impact on almost overall human civilization.
In this way, machine learning may allow AI to become more and more advanced. And, who knows, in the future, all of the robots may have some sort of emotion or feeling. And, if you think about it further, maybe in the far near future, it will be possible for people to program their own emotions into a robot-like mind.
It’s going to be hard to predict the changes that machine learning is going to bring because they are so varied and widespread in their area of change, but it’s still worth thinking about them all before they happen.
As time goes by we will be able to use ML-Based AI for more things than ever before. When it comes to the near future and everything that is to come, both the possibilities and opportunities are going to be mind-boggling.
We might not see it as much today, but I assure you shortly both will play a very big role in how humans live their lives.
In fact, Machine Learning may end up determining how long time each of us has on this earth!
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