The future of Machine Learning

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.

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:
Image Source: Agencies

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 com­pute, 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 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.

At 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 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 the 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 which 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 com­mon place

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 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 sce­nario 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 feel­ings 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 the blockchain tech­nol­o­gy, it will also be pos­si­ble for VR com­pa­nies to save on costs by work­ing more effi­cient­ly, waste less resources and reduce 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 than any­thing else. Machine learn­ing is going to help with expert advice like this:

a. Surveys

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…

7) Gene-editing

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, they will 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 business.

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 the 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 have more mean­ing­ful con­ver­sa­tions with each oth­er. This could lead the way back ‘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 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 pre­dic­tions 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 pre­dic­tions 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 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 an 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 too.

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, it 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 has 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 our­selves can pre­dict now that machine learn­ing is going to change many things in the world in the very near future.

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, it is like­ly that Machine Learn­ing will per­form far bet­ter than you. It maybe 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 dis­eases that are extreme­ly rare, 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.


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 wor­ry­ing 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
Advance­ment 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 few are these, machine learn­ing will have strong impact in 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 feel­ings. 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.

Let’s conclude,

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 in the near future both will play a very big role in how humans live their lives.

In fact, I believe that Machine Learn­ing may end up deter­min­ing how long time each of us has on this earth!

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