The technological revolution in artificial intelligence and machine learning has brought great changes in the lives of people. Technology has made almost all the echelons accessible to people.
It has not only simplified the lives of people but reduced their workload and dependency. How machine learning and artificial intelligence can help mankind.
Mechanization has ushered in lucrative prospects for people from almost all walks of life. As a result of this cogent and pragmatic convenience stirred by technology, we are willing to make a beeline for any sort of technological innovation.
What is Artificial Intelligence?
What is artificial intelligence? Learn about definition, examples, and applications of artificial intelligence and Machine learning.
Artificial Intelligence is one such cog in the wheel of tech-progress. From self-driving cars to weaponry of artificial intelligence, it has brought great strides in the lives of all and sundry.
To the oblivion of many, Artificial Intelligence or AI is not only robots with human-like characteristics; it also envelops anything from IBM’s Watson to Amazon.com or Netflix.
But what exactly do we mean by Artificial Intelligence and what does it connote in terms of AI challenges?
Machine learning means feeding a lot of data to the computer with all the answers. When you later need similar responses from the network, the system can give answers.
John McCarthy is considered as the father of AI. He explains it as “The science and engineering of making intelligent machines, especially intelligent computer programs.
Artificial Intelligence is a way of making a computer, robot, or software thinks intelligently as intelligent humans think”
Machine learning is a part and participle of artificial intelligence. Artificial intelligence is vast and it’s the only functional area is machine learning. Although machine learning and AI have the same components.
Concept of AI.
Mode A – When two blocks of information are very close to each other, they would be put in the same bracket.
The computer might also decide that group A of information can reasonably be placed in any of the 10 brackets, so that is mode B.
Then further, machine learning determines a central focal point of information, and how close is the data from the central location.
Powerful Examples of AI.
This information can be used to determine an anomaly in medical knowledge. The computer has millions of data about well and healthy people.
The computer will notify an anomaly or a disease if the computer sees a brain condition that it is different than stored data. Thus, machine learning has medical uses.
Machine learning has other uses. The machine should be able to derive all characteristics of an image and how it is perceived in its environment. How vectors work, how the image would look inverted and what would it mean. This is called image understanding and is more in-depth learning.
The computer can look beneath at the cellular blood level, and see if the person could get a heart problem. It can identify with simple photo click.
AI to track student’s progress:
It could also have educational uses. Machine learning and artificial intelligence could also have educational uses. The computer knows how well the students should score. Students are segregated and placed in the separate category according to their performance.
Those students can thus be awarded and rewarded accordingly.
AI for Face recognition:
With photographs what also happens is the computer is not only doing smart things, it is doing different things such as by seeing enough photos of a person A, the computer can start to decide if the person in front of the laptop is person A or not.
This is image recognition. This is the next level, and it is part of Artificial Intelligence more specifically called machine learning.
AI in Amazon
Amazon used AI in its myriad transactions by the dent of which it was able to make huge profits. Over a period of time, its AI has been transformed into something which nears finesse.
Its AI has refined its algorithms and accurately predicted the interests of the consumers on the basis of the online behavior of its humongous patronage.
Though it has been said that Amazon wants its AI to reach a level where it ships the products before we even know we intend to buy it. Given the circumstances, it is definitely on the cards.
AI in Boxever
Boxever is a company co-founded by Dave O’Flanagan that relies heavily on AI to dispense amazing travel experiences to its customers and dole out micro-moments to them.
It has maintained dominance in the industry because of its robust AI system that suggests to its customers to embark on new travel journeys.
AI in Netflix
Netflix is another coveted endeavor. Play on-demand is the strategy followed by these streaming services. Customer’s preferences and tastes on the basis of its highly predictive technology.
This nifty system which analyses billions of records presages the type of movies or series a person might watch. This happens by monitoring the previous choice of films or other videos of a user.
How can AI pose danger to us?
Use of AI to cause destruction:
Weaponry of AI is a concrete example of ill-use of AI. We can see real examples of the devastating use of AI in the form of its weaponry. Major countries try to merge AI in their weaponry system.
For e.g. using fire-and-forget missiles, deploying unmanned terrain, naval, and aerial vehicles, usage of surveillance drones, robots, production of collateral damage estimations, etc.
If by any chance these AI systems fall into the hands of a wrong person, the devastation will spread like a wildfire throughout the world causing mass casualties.
At this juncture, when countries are running in the rat race to assert hegemony and filch the oil and water resources, weaponry of AI portends an AI war which in turn would lead to great fatalities.
To ward off any interference to AI by the enemies, these AI arms would be created in a way that makes it difficult for humans to switch it off.
Use of AI to do something beneficial but it deploys a dangerous method to achieve the beneficial objective:
One of the most challenging things that creation of AI has meted out is the alignment of its goals with ours.
For e.g., if you wish to operate an AI car and ask it to take you to your school or college fast, it may do so but in a way totally different than what you conceived.
So, just imagine AI programmed to accomplish a geoengineering project. The very obvious answer is that it will cause havoc hither-thither.
Cyber-Security Challenges in artificial intelligence.
The recent attack of ransomware enveloped thousands of computers across different countries of the globe and included India and Europe.
In the world, most countries like the UK, Russia, Spain, and France are more concern about the security of sectors like Consumer, Shipping, aviation, oil, and gas.
In India, the Jawaharlal Nehru Port Trust had to close the operations in three terminals.
Cyber-attack Challenges in artificial intelligence.
These recent examples of cyber-attacks evince one thing very clearly: vulnerability of AI to manipulation. In other words, algorithms are also prone to bugs, malware, and attacks.
In such cases, there will be mammoth losses and may even result in irreparable injuries.
Since cyberspace is a virtual space, it is very difficult to find physical evidence and nab the culprit.
Cybersecurity entails a lot of technicalities which the layman is not aware of and as a result, he may be very susceptible and high on the radar of attacks.
Since AI often operates through a connected device, what will happen if any of the devices get affected?
Security breaches and privacy breaches have been making headlines recently and pose a big question mark on AI which of course thinks like a human but does not have a soul.
Food for thought
Given that weaponry of AI is inevitable, aren’t we forgetting that the very purpose of creating AI was to reduce human dependence on unproductive labor and to increase the efficiency of resources?
Apart from destructive AI one should efforts to utilize it for social good such as public welfare, health infrastructure, sustainability, etc.
At the end of the day, each one amongst us is aware of the dire need to invest in AI for social good, then why are we stressing on shoving AI to an extent where they dictate us? Do we know that machines can’t think but can’t humans think too?
Career future in machine learning.
Machine learning data operators would be scientists for the most part, and if you are planning a career in machine learning, yours should have at least 2 years or more of experience, and your package would be very high, and your future would be very bright.
Educational qualification required
You need to have basic programming knowledge, there are online machine learning courses given online. Online competitions held in the field of machine learning, and many a student who can afford higher studies go to universities like Stanford.
The professors say that machine learning can create magic and the staff to do so is not a lot so it is like a new field that is coming up just like robotics was a few years back, and if you like solving algorithms then this branch is for you. The coding language of choice when you want to get into machine learning is Python.
Machine learning with Python.
Why we are saying, Python is the best programming language to follow when you want to get into machine learning is because there is enough background and support available for machine learning when it comes to this language.
The second thing you need to learn is what the difference between descriptive and inferential statistics is. When you consider the mathematical average, it can be said it is part of descriptive, and inferential means applying data about a sample to a larger population so this sample data should be accurate. Research scientist also
Machine learning with Language ‘R’
The second language that a scientist should know is the language, ‘R’. But, this comes later, and you should first brush your knowledge of calculus, algebra, and probability so that you can get on to the tougher concepts later in life.
There are two paths to get into the machine learning domain. First is that you are a software engineer of repute, this way, start taking up small machine learning projects till it becomes your first-hand job.
Build your own road map, and you should see that machine learning is the tomorrow of things just like the internet of things.
All things being one, you would see machine learning grow into a thing of 2067 which is 50 years down the line.
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