The wave of excitement over artificial intelligence is spreading, and like a tsunami is anticipated to touch every possible surface. And yet as this wave crashes down, many consumers and companies struggle to identify the subsets of AI, and what these distinctions mean. Decision-makers are still unclear about the difference between ‘machine learning,’ and ‘deep learning’, and the significance that this distinction has in keeping their system’s users and data safe.
Machine learning and deep learning are neither synonyms, nor competing technologies, and yet the variation between them makes all the difference for companies attempting to harness the power of data while trying to determine the best path to success. There are lots of wrong terms and usage, and like any great trend, people end-up following them in the wrong way. In this case, by incorrectly applying terms and mainly by focusing implementation on methods that do not bring any real value and have no technological edge. A good example of this is the use of common open source libraries, which is often done the wrong way or for the wrong domain purpose (we’ll expand on this more, further on).
With all this surrounding confusion regarding machine learning vs deep learning, we bring a clear distinction between the two; we demonstrate their distinctive objectives and the respective purposes to which they lend themselves.
Back to the Basics: Defining Deep Learning and Machine Learning
Deep learning is a subfield of machine learning, which is itself a subset of AI. Deep Learning is trying to mimic the brain’s behavior in the same way that the human brain learns (by being agnostic to the input) – taking in all the data and learning from it continuously and intuitively.
With the ever-growing availability of data that is increasingly informing many disciplines, deep learning is uniquely disposed to leverage massive volumes of data for more intuitive, accurate, and real-time insights. The training of the deep learning algorithm necessitates scaling hundreds of millions of data samples, and improvement is a case of ‘the more data, the merrier’ (as long as your training data is labeled well, and your dataset is qualified).
Deep learning is unique from machine learning in that it is the first, and currently the only, method that is capable of training directly on raw data. Traditional machine learning requires feature engineering, where a human expert effectively “guides” the machine through the learning process by extracting the features that need to be learnt. As it’s based on human analysis, it’s highly limited and relies solely on specific known use cases. In contrast, deep learning can dive into the raw data of the file, without explicitly being told to pay attention to engineered features and analyzes all the available data.
Cybersecurity on Steroids
In its application to cybersecurity, the deep learning model is able to analyze the millions of different possible files and attack vectors. As the training dataset gets larger and larger, the deep learning algorithms continuously improve. It’s this unique ability to pick up on patterns and nonlinear correlations in the raw data that are too complex for any human or traditional AI to pick up on, which gives deep learning its inherent value.
This ability for in-depth analysis predisposes deep learning to cybersecurity on multiple levels. Firstly, because the entire file is analyzed, it is not limited to selected information that has been extracted. Secondly, aggregating simple non-linear units into a deep network enables the capture of hierarchical dependencies between features which produces empirically better accuracy. Lastly, the abundance of data in which to train with, means that there is great scope to improve accuracy and reliability.
The combination of these analysis features provides numerous valuable outcomes for the future of cybersecurity:
- Deep learning-based model is able to produce a much higher detection rate and lower false alerts, effectively eliminating alert fatigue and reducing costs.
- This is the case even for new and previously unseen cyberattacks. The broad coverage of attack vectors provides verdicts that are more accurate than the best traditional machine learning solutions available.
- It provides the ability to scan any type of file statically in pre-execution mode. Unlike detection and response tools that only act when something is already running, which in most cases is too late.
- The platform-agnostic solution is able to assess for unlimited types of attack vectors, while it provides protection irrespective of the operating system or the device (be it mobile, endpoint, server, network). Doing so, from a single unified platform.
Other Applications of Deep Learning:
Despite the high levels of entry, the adoption of deep learning is accelerating and we see it being applied within particular commercial sectors:
- Autonomous cars: The development of image recognition is paving the way for autonomous cars to unlock with facial recognition, alert drivers to potential hazards, and gaze tracking for driver distraction alerts or and driving autonomously. While this technology has been under development for some time, only during the last few years has the pace towards obtaining industry regulation quickened as scientists now have the data to demonstrate that autonomous cars are safer than human drivers. As more and more autonomous cars are operating on the roads and communicating with each other, it will contribute to much more available data. And as we mentioned earlier, the more data the better, as it leads to better accuracy, minimizing the risk and machine false alarms.
- Healthcare: Similar to the domain of cybersecurity, the health industry lends itself to deep learning. This is because of the copious amount of information that comes into a medical diagnosis, with tremendous lifesaving results to be gained with even classic machine learning algorithms and implementations. A good example of this is MobiGuide that enables remote patient care, by providing patients with a device that monitors their health, all data is transmitted to a DSS (Decision Supporting System) that is able to analyze all the available data, provides a diagnosis as well as develop a treatment plan. This information is then relayed back both to the patient and doctor for review.
- By feeding some of the raw data into deep neural networks without any pre-processing, researchers are getting even greater results. An example of this is IDx-DR a diagnostic system that detects signs of diabetic retinopathy in retinal images. The medical expert system, which has FDA approval can be used without a doctor, to analyze medical images of eyes to determine if treatment is required.
And Yet Deep Learning is Not Always the Answer
While it is true that since 2012 (where a report on the usages of deep learning for state-of-the-art image recognition was published) deep learning has become increasingly commodified, its use has so far been limited to certain fields; of which object recognition is the most persevered application. In the realm of visual object recognition, the machine now outperforms the human being. As training continues based on millions of images, machines are more successful at identifying an object inside images than humans. The most well-known benchmark for humans vs. machine on a respectful dataset is ImageNet which achieved an error rate of 5%, Google superseded this benchmark with a demonstrated 4% error rate, and this has been superseded by Microsoft with a publication demonstrating that their deep learning algorithm achieved an outstanding 3.57% error on the ImageNet test set.
But this success does not mean that deep learning has been able to successfully enter every field of knowledge. The cost in time and resources involved in building deep learning models entails numerous considerations that would not justify the value of a deep learning model over traditional machine learning models.
- The domain needs to be an area of knowledge that a traditional machine learning model cannot solve or in which it can reach a limited Considering that machine learning algorithms are much easier to use, if it can adequately do the job, it is not justified to go the extra mile with a deep learning model.
- The domain must involve huge amounts of data in which to train. Unlike classical machine learning implementations which rely on certain features, deep learning, necessitates a significant amount of knowledge to build the datasets. So that with the right data and data scientists, you can always expect to increase your recall (until a certain threshold, which would almost always be lower than 100% accuracy) and lower your False Positive rate in correlation to the increase in the size of the datasets.
If the input data is flexible, then you should be able to adapt the data to meet the training needs of the fixed size of neurons in the input layers. For example, an image that has too many pixels can be easily reduced by minimizing the size of the image.
When looking to develop a deep learning framework, there are a number of publicly available options, including TensorFlow by Google, PyTorch by Facebook, and CNTK by Microsoft. The availability of these frameworks has significantly boosted research in deep learning, as they enable applications and programs to be implemented directly on these frameworks and experiments to be executed without having to write a single line of low-level code.
However, there are distinct advantages of using a framework dedicated to a particular purpose and objective, instead of public frameworks that are built more for academic educational purposes. Firstly, while a public framework may be designed to have a faster model training time, a framework built for commodification also has an emphasis on a faster prediction time. Secondly, using a public library framework, the agent file would be enormous because there are so many dependencies which it relies on, however, a dedicated framework involves a smaller subset of algorithms (that is more focused to achieving its objectives) enabling the agent to be small enough for commodification.
Beyond the Hyperbole
With all the buzz around AI, one would be sure that this new field of deep learning will change every aspect of our everyday lives, but the reality is the fully realized potential of deep learning is still a long way off. This is partially due to the high barrier of entry which will limit the players and the fields in which they interact into image recognition, speech recognition, natural language processing, recommendation systems, and cybersecurity.
Saying that, as deep learning applications continue to expand, companies will ignore these developments at their own peril. The industries that are impacted by deep learning stand to benefit enormously, as the inference model of deep learning continues to supersede human capability, powerfully pushing forward the frontier of technological prowess.
Like any new evolutionary technology, in the coming years undoubtedly, we will find visionaries adapting it and running with it to achieve spectacular implementations, such as analyzing cancer cells and curing other horrible diseases to the result of potentially enabling humans to live longer and safer. Deep learning could be used towards empowering law authorities to effectively fight crime, whether it be detecting fraud or preventing money laundering, by keeping well ahead of criminals. However, deep learning could also be used towards nefarious purposes, either by nation-states or independent hackers, who will try to take advantage of implementing AI to achieve the fulfillment of warped and dangerous ideas. How the future of deep learning will unravel, we’re still yet to tell.
As Andrew Ng, one of the top researches in the Deep Learning field said: “To the AI Community: You have superpowers, and what you build matters. Please use your powers on worthy projects that move the world forward”.