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Artificial intelligence (AI) and machine learning (ML) are popular terms, which most of the time we hear being used in movies. But now they are in your real-life making your livelihood more modern and sophisticated. Today, we will talk about various Machine Learning Challenges.
On one hand, AI provides significant opportunities to accomplish business impact with machine learning; on the other hand, it has quite a number of machine learning challenges as well. Issues in machine learning mainly occur due to the Adhoc rise in the awareness of the technology and its implementation. This blog runs-through the open problems in machine learning and great challenges in it faced by industries at present.
But it happens the other way with machine learning engineers. This happens because of the over expectations set by the entrepreneurs, designers and managers without knowing the actual capabilities of machine learning.
They assume that algorithms can be learnt quickly and result in simplified workouts to complex queries.
AI is not magic because it has super-intelligence and can create wonders. People started to presume AI in such a manner that it has the potentiality to resolve any kind of problems within a jiffy. But in reality, it is not true and no way is easy at all.
Everyone should understand the fact that the commercial use of machine learning is very new, and it has steep learning methods.
There could be a number of varying parameters between one AI technology and the other, depending on changing companies’ requirements.
Overtraining an automatic mechanism would always lead to complications and limits to function with deep learning algorithms.
Let us see What Are the Problems of Machine Learning in the following section, and how it impacts your business processes.
In other terms, memory networks are known as memory augmented neural networks, which require huge working memory to save data.
This neural network should be linked to a memory block that enables the network to read and write as well. This is one of the biggest problems in machine learning that needs to be overcome.
If you want the AI to be effective, adopting a better method for networks is essential. By doing so allows the network to identify facts, protect them and access them seamlessly as and when needed. So, this is one of the massive Machine Learning Challenges.
Fragmentation of Sentences into Tokens – NLP involves many applications like sentiment analysis, language translation, grammatical error detection, face news detection, etc. This involves breaking down of complex sentences into tokens or small chunks, or more commonly known as tokenization.
POS tagging – POS or Parts of Speech tagging is basically assigning of one of the parts of speech to any given word. In simple terms, its just labelling each word in a sentence with its appropriate parts of speech. NLP does not have the feature to tag POS with the words which poses another challenge for it.
Vocabulary building – Solving Multi-Level (MLC) problems requires an understanding of language and, in turn, a good vocabulary. When new users or products comes into the scene, which has no history records, the machine fails to read it in the absence of a good vocabulary.
Data related challenges – Another notable challenge in NLP is data related problems. It usually becomes quite difficult to extract information from vast datasets. The relevant and correct information from semi-structured or unstructured data is a challenge for the program.
Until now, we could not see any implementation of human visual systems integrated into neural networks, so the potential of machine learning could be utilized to its fullest.
If integrated, it will contain a rich set of features that will definitely meet your AI expectations. But it is not happening at the moment with machine learning. Currently, ML focuses on small blocks of input stimuli one by one and finally, collates the results.
To experience real progress in a project, it is important to understand how deep nets training can help.
Although machine learning has managed to come so far, it is still unrevealed how deep nets training works in ML.
In detail, deep nets training can be termed as the set of methods or procedures in which training or learning protocol is carried.
It is a way in which machines automatically detect how to classify the given input data. In the Artificial Intelligence era, understanding Deep Nets Training can be possible.
One-Shot learning is something that refers to the categorization of the objects. This is one of the main challenges in machine learning which is completely in the hands of neural networks to improve based on continuous learning.
There is no one-shot learning available for applications of neural networks because it requires a huge amount of data to learn in the traditional gradient-based networks.
Rather than getting into too many cases for learning, it will be quick and convenient with one or two examples. With one-shot learning, now the computer vision is working far better than it did in the past.
If there is any way that we can figure out to enable this reinforcement learning to manage robots, it is definitely possible to create characters like C-3PO in real-time.
In the near future, Reinforcement Learning (RL) is going to do unimaginable things especially on building intelligent agents who can perform far better than humans in both economics and as well as in handling multiple tasks at a time.
Experts were able to find a solution for image classification, whereas now they intend to resolve semantic segmentation, which is an interesting machine learning problem.
The primary goal of semantic segmentation in machine learning is to name each pixel of an image according to its category of what is being displayed.
With the aid of semantic segmentation, one can modify the existing image into a more useful one and is easier to analyze.
Apart from just portioning the images during the semantic segmentation process, each of those portioned images pixel by pixel gets labelled pointing to its characteristics.
Currently, we feed machine learning only with static images and is still under process to make use of and apply video training data. It involves more listening and observing to make machine learning work better.
This will be a great benefit if improved because we humans are more addicted to watching our dynamic world rather than being stagnant. It is one of the Challenges In Machine Learning.
Also, video datasets tend to be richer than static images, so there’s nothing wrong with allowing ML to be beneficial for humans on observing the dynamic world.
It makes the identifications of objects very hard for algorithms to properly detect due to image classification and localization in computer vision and machine learning are still flawed.
This can be easily resolved by investing ample time and more resources and bringing this machine learning problem to an end.
Object Detection includes face detection and pedestrian detection which plays huge roles in retrieving images from surveillance video.
The object detection generally falls under the category Machine Learning (ML) or Deep Learning (DL) based approaches.
It may take some more time for AI to even up with big data and computer power. So, democratizing AI is one of the massive Machine Learning Challenges.
If it happens, then the significant intelligence takes on the entire machine learning problems on its head and resolves automatically without any interdependency.
Democratizing AI, it is an idea which gives everyone the opportunity to access and avail the benefits offered by Artificial Intelligence.
In simple words, it just explores the positivity of the AI and will reach more users and companies.
But they may not be accurate or the machine learning could not realize the exact object when seen in the picture.
As algorithms were simple, with trainers specifying the characteristics of an object, machine learning was quite easy.
But deep learning algorithms are unique and not simple as well. They create their own level of understanding by developing data representation following a hierarchy.
To do this, the ML has to analyse a huge amount of data and so it makes the job of neural networks easy to recognize the objects with astounding accuracy.
This is one of the biggest and primary challenges in machine learning. There are many people who get attracted to the machine learning industry.
But only a few specialists are capable of developing this technology. Moreover, a skilled data scientist may not be versatile in software engineering. This kind of problem is worse than any other problem with machine learning.
Several companies across the world, who have adopted AI and ML as their primary resource of application development, have reported that only a few researchers and practitioners are handling AI worldwide.
Google, Amazon, Microsoft or Facebook hires only the top-skilled engineers. Such companies make more profits out of AI but also bring the average skilled specialists to the market.
We have already specified that neural networks require huge sets of data. Right now, the storage of data does not seem to be a problem anymore, whereas collecting those data may take most of your time.
This again depends on what you plan to feed the system with. Also, it is not really easy to prepare data for algorithm training.
You should have clarity of what you want your algorithm to solve, so you can plan classification, regression, clustering and ranking furthermore.
At the same time, you cannot use the personal data of your users as well, as it is a matter of privacy and security of information.
Most of the people may not be interested to provide their personal data too. So, data-handling is again one of the machine learning challenges.
Every technology when developed and introduced requires enough time to settle down.
In such cases, AI and ML also fall under the same category and even require more time than that of normal applications built.
It is not a superhero or some supernatural power that gets things done within a blink of an eye. AI technology is still young and under the buffering stage.
Although you may find it technically feasible, sometimes, it may not be ready to use for production.
Unlike other technologies, machine learning has multiple layers that demand more time for proper development and to yield the desired output.
You might have business goals and respective strategies planned to implement the same. But you can calculate the number of days that it may take for the completion of that application using a traditional technology method, which is not the case in machine learning.
Also, the ML engineers and professional data analysts can’t guarantee you on replicating the training process model again. In simple terms, engaging with the AI project is a highly risky one.
Expectations towards AI is a never-ending process because either you might get your own ideas in plenty or get inspired by others’ ideas and practice towards implementing it.
Though Machine Learning and Artificial Intelligence come with both pros and cons, people are not ready to accept the negative connotations in AI and ML because of high expectations.
But you also have to understand that these expectations you try to enforce on neural networks is quite new and either will definitely take time or cannot be possible without any interdependencies.
Since machine learning includes large data processing, it makes use of GPUs without which it finds difficulty in execution.
But GPUs also face a supply and demand issue because every AI needs a GPU for interpreting and functioning.
Sometimes, big companies also may not provide sufficient GPU as per their employees’ requirement. Imagine if a team performs machine learning off the GPU side.
It definitely consumes much more time to train their models. Also, it will be very difficult to retrain or to update the models often according to the needs.
Ethically AI may go wrong if there is a fault in your teaching itself.
You should provide proper training with brave and good intentions along with machine learning; otherwise, it is going to be the biggest problem in your life.
The best example would be training your ML drones for the army to defend from the enemy’s army.
By mistake, if you train your drones to kill people in the military, then it is considered as bad intentions in teaching.
This particularly might create a major problem with machine learning in hospitality and medical treatments. Machine learning naturally does not intend to create any harm to people or society.
But developers can do that. In some places, developers create algorithms for their own benefit and do not care about societal needs. Let’s take Health care industry as an example…
We can consider the medical algorithm as the best example of developer bias. Consider a medical algorithm suggesting expensive treatments instead of suggesting the best.
This actually leads the way resulting in a higher price or complicated process of medical treatment for the patient.
Ethics of a region may change over time or even sometimes quickly too. It all depends on the trainer, who looks at the situation in what manner.
Not every eye looks at a problem in the same way and due to this; the solution for every situation may vary.
This impacts the way that algorithms perform according to the situation.
The best example for understanding ethical relativity is how different countries significantly handle the war against terrorism.
Also, ethics can differ between the two groups within the same country as well.
To summarize, artificial intelligence is not a fairytale that you can do anything you want anytime.
Every technology requires enough time to understand and settle down in a particular project and reveal fruitful results meeting your expectations.
Most of the machine learning problems occur due to trainer’s over expectations in anything pertained to AI.
We hope you all had a great time reading this article and it proves to be of great benefit for any Python Development Company and companies looking to hire python web developers in the near future. Thank You.!
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