Though it is believed that tech enthusiasts are passing through the golden age of Artificial Intelligence, engineers and scientists still need to explore miles progressing on their journey to Deep Learning projects.
Either it is calibrating time-series models, processing Bayesian deep learning models, or defining metrics for uncertainty evaluation, the existing capabilities of machine learning are all overestimated. In other words, the present expectations associated with machine learning are more of mystical assumptions.
Especially when we talk about the commercial applications of machine learning techniques such as deep learning, the approach is still very young. It needs an extensive amount of well-prepared and organized data to attain the accuracy we look for. This means understanding and adopting the dynamics of machine learning require rich access to resources and time with provision for risks.
Above all, the existing deep learning algorithms are suitable for working on training records and process the information using millions of parameters of the neural networks. This setup could turn vague when applied to new data. Thus, there are so many challenges associated with the idea of Deep Learning that must be worked on to attain futuristic outcomes.
Let us quickly dive into understanding some of the most significant challenges associated with deep learning and how a practical approach to AI testing and machine learning best practices could help attain progressive results. But before we start with understanding all the novel challenges, it is necessary to understand all the future advancements associated with deep learning.
The Future Innovations Surrounding Deep Learning
Deep learning is one of the most significant tech advancements. Going by the definition, the technology works by classifying the information imitating the crude behavior of the human brain through layered neural networks.
The neural networks then process the given set of input units to be fed on raw data like pictures, sound, or text to map the output nodes. And some of the most futuristic innovations that are surrounded by Deep learning could be listed as:
- Attaining General AI or Hybrid AI
- Supervised learning
- Deep Learning is likely to give an all-new face to the healthcare and medicine industry with the power of data
- Also, more innovation with the help of deep learning could be seen in the field of Fraud detection, virtual assistants, visual recognition, and entertainment.
All in all, deep learning has gone too deep into our day-to-day routine, and examples of the same could be tracked from some of the most well-known applications that are leveraging speech recognition, NLP, and image processing to solve problems more accurately.
However, reaching out to the extremities of deep learning and goals associated with Hybrid AI need thousands of hours of research, data mining, experiments, programming, failures, and restructuring, and everything that translates into the novel challenges associated with Deep learning implementations.
The Novel Challenges Associated with Deep Learning
When we talk about futuristic and intelligent machines that have their own brains, Deep Learning takes the front seat in any such development process. Even the most advanced AI applications are driven using the best of deep learning technology.
There are some novel challenges that are required to be addressed for reaching the futuristic applications of Deep Learning.
- Machine Learning As An All-new Alchemy
The initial days of machine learning had some shallow practices where decision tree algorithms were ruled to make predictions. For instance, “If something is orange, citric, and is made of pieces, there’s high likeliness for that to be an Orange.” Though these models didn’t have extraordinary intelligence to recognize differences between a Mandarin Orange or Cara Cara, it was easy to understand how they worked.
But Deep Learning is different as it works on hierarchical data representations. However, the only thing that restricts creating such technology is the lack of understanding of how it can be achieved.
The problem is defined as Black Box as AI supervisors have the knowledge of input and outputs while engineers know how a single prediction is nurtured, but no one understands how the entire model works. And this has turned to be a hurdle into the path of creating futuristic products like automated credit assessments, driverless vehicles, and medicine development.
Moreover, several researchers have claimed that AI is likely to encourage negative emotions and fear with people being scared of objects or technology behaving just like a human. Thus, it would be nothing wrong to say that this “Uncanny Valley” has enabled humans not to accept technology that can do typical human stuff.
Though the situation will change with the generations becoming more familiar with the digital environment surrounded by Machine learning, beating Black Box at present is a challenge that engineers and scientists are trying to overcome.
- Resource Deficiency: Lack of Expertise
Though deep learning, has managed to capture the attention of business and tech enthusiasts across the globe, only a few specialists have managed to master the development process. Moreover, data scientists who have the proper knowledge of machine learning, most of them lack the knowledge of software engineering.
The situation has now turned worse as Element AI in a report shared by Bloomberg in 2018, estimated that only fewer than 10,000 experts have the skill to tackle AI research, and the most prominent industry players like Amazon, Facebook, Google, Microsoft had already reserved the best out of stock.
This means a massive jump in the salaries for AI experts, making machine learning and deep learning much difficult technologies to foster. New York Times reported that experts working on AI with only a few years of experience tended to earn up to $500,000 every year back in 2017, while the most experienced ones were likely to earn as high as NBA superstars.
- Need For Extensive Training Data
Training Deep Learning models need access to extensive data sets. Though it may appear to be an easy task to accommodate or store information, purchasing the most relevant data can be expensive. Besides, it is equally consuming to prepare data for the training sets, especially when you need to work on clusters, regression, and classification.
The process even involves the need for consistent formatting and mechanism to collect information so that tasks like aggregation, attribute sampling, and record sampling could be done, which may need data decomposition and rescaling. All in all, the process needs to buy the time of the most skilled engineers, which might feel heavy on your investment graph.
In case the organization aims to use their stored data for the process, chances are high that privacy issues intervene, leading to legal issues with the people to whom the data belongs, even when they shared consent at the early stages. Thus, using personal data could lead to risks and unwanted expenses on data protection regulations like European General Data Protection Regulation.
- Optimization Requirements Related To Deep Learning Model Performance
Though machine learning may appear to be a magic wand that tech giants like Alphabet and Microsoft are trying to aim with TensorFlow and ONNX, running these systems needs data from around the world. Moreover, all these efforts could not deny the fact that deep learning is very young, or rather we can say it is immature to be called production-ready.
Especially, when we talk about working on all the hyperparameters associated with the learning process, any changes made on values could lead to massive alterations in the model performance. In other words, the inability to work on hyperparameter optimization could significantly hamper the performance of deep learning models. Besides, the lack of knowledge and resources to detect the change requirements could affect the optimization tactics making way for performance degradation.
- The AI Bias & Roll-Out Pressure
Since Machine Learning is all about utilizing the best of AI, the future of machine learning and AI systems entirely depends on the quality of data on which systems are trained. Moreover, the data which is used in the production process often involves the issue of bias as it comes from organizations that have filters on demographic specifications.
- Ample Time & Planning Requirements
When we talk about software from the past, the development was quite easier as compared to modern software integration requirements where ML and AI are ruling the tech world.
Moreover, the bigger challenge of working on deep learning solutions is time requirements. On top of that, the uncertainties involved in the development process make it difficult to attain precision. Even the most refined engineers who have been working on machine learning for years tend to struggle with analyzing data sets and confirm that the training models could replicate results for different data.
Best Practices To Tackle Deep Learning Challenges In The Future
- Work On Your Training Data
The primary objective of the machine learning engineers should be identifying the cases where data is misclassified by the algorithm. These are most likely to be edge cases and mislabels which need extensive testing on training data and models. The process of analyzing and processing training data with the algorithm usually aims to work on use cases where the model is not able to perform better than independent humans.
- End-to-end Analysis & Deployment
The next most important factor that should be worked through Machine Learning includes working on the simplest areas of the entire deployment plan. This would help you overcome any model complexity as you progress in the documentation, as real-world modeling often needs you to work much harder in real scenarios. However, the end-to-end analysis allows you to work on every piece of complexity.
Besides, any deep learning models that are worked in regard to the end user’s perspective could help to identify crucial issues with the model. Also, the approach helps to reassess the training data collected so that any issues with the information could be worked.
- Find Ways To Work Inevitable Cases Of Algorithm Fails
When we say Deep Learning, a good number of efforts usually fail, and therefore it becomes necessary to handle all these factors effectively. However, to build a confidence score for the test cases that work well, leaning on the batch process could be done. This would aid the collection of high-quality training data, accommodate less confident predictions, and highlight red flags.
Concluding It All…
Aligning with the Deep Learning technology for unleashing contemporary machine learning requires a thorough understanding of the limits. Achieving the technique to master the way machine learning feed on data could help turn some of the most futuristic technology changes. This could look like a future where self-driven vehicles are going to rule the roads or virtual assistants making way into our day-to-day operations at home or work.
Thus, taking over the novel challenges is vital and only needs an approach that could recognize how the AI industry, despite the extensive risk, has extensive rewards to offer. In a nutshell, technology enthusiasts only need to be patient, cautious, and respectful towards challenges that machine learning possesses while making way for the most predictive and precise solutions that have no empty promises involved.
Above all, there is a need for using existing machine learning technology to create deep learning of the future. The process could involve accelerating automation testing while encouraging the continuous quality culture, data-driven DevOps, and of course, autonomous end-to-end tests.
