Artificial intelligence (AI) and deep learning are trending technology topics, and much media speculation relates to whether AI will eventually replace human labor, such is its potential. However, with research showing the number of jobs requiring AI skills growing by 450% since 2013, it appears this speculation is somewhat unfounded. While AI is not exactly a new concept, advances in its accessibility and computer science have brought it to the fore in recent times.
Any article that attempts to discuss both AI and deep learning must first distinguish between them because they are not the same thing.
The definition of artificial intelligence is quite broad and simple ‘it refers to the simulation of intelligent behavior by computer systems. Such behavior could encompass any range of tasks for which human intelligence is normally necessary, such as visual pattern recognition, language translations, and decision-making.
Deep learning, on the other hand, uses artificial neural networks inspired by knowledge of human brain biology, from which emerge algorithms that are highly efficient at solving classification problems. You can think of deep learning as an aspect of AI that uses complex hierarchical neural networks and lots of precise data to make machines capable of learning things in a similar, yet cruder way than humans can learn. The market value of deep learning software is projected to grow from $20 million in 2018 to $930 million by 2025.
Read on to find out about five important AI and deep learning challenges for 2018.
1. Deep Learning Needs Enough Quality Data
Deep learning works best when it has lots of quality data available to it, and this performance grows as the data available grows. However, when enough quality data simply isn’t fed into a deep learning system, if can fail quite badly.
In a story that emerged during 2017, researches fooled Google deep learning systems into making errors after altering the available data by adding noise . While the errors forcefully unearthed by the researchers related to trivial matters, such as mistaking rifles for turtles in image recognition algorithms, this still exemplifies the dependence of deep learning on the right quantity and quality of data for it to work accurately.
With small input variations in data quality having such profound results on outcomes and predictions, there’s a real need to ensure greater stability and accuracy in deep learning. Furthermore, in some industries, such as industrial applications, sufficient data might not be available, limiting deep learning’s adoption.
2. AI and Expectations
There is quite a discrepancy between the layman’s expectations of AI technologies and the actual potential uses of AI. The perception that media tends to report is one of the super-smart computers with cognitive capabilities that will eventually replace many jobs humans would normally do.
However, the computer and data science industries have a challenge on their hands to address these lofty expectations by accurately conveying that AI is a tool that can enhance productivity as opposed to something that will replace entire human roles. Automation of mundane tasks, data-driven predictions, and optimizations are all things AI can do well. However, in most instances, AI cannot replace what the human brain brings to the table, particularly in highly specialized roles.
3. Becoming Production-Ready
With the latest stats showing 80% of enterprises investing in AI, there will be growing pressure on organizations and their developers to transition from modeling to releasing production-grade AI solutions. After all, the significant investments in AI need to translate to value in solving real-life problems if they are to be considered worthwhile.
The focus will be on operationalizing AI capabilities through upgraded technology infrastructure, addressing security concerns such as informational integrity and the security of the AI platforms, and ensuring the high availability of AI solutions so they can deliver results, predictions, etc. when needed.
A critical goal of AI in the enterprise for machines to assist executives and key stakeholders in making important decisions; both strategic and tactical.
4. Deep Learning Doesn’t Understand Context Very Well
The word deep in deep learning more refers to its architecture than the level of understanding that these algorithms are currently capable of producing. For example, a deep learning algorithm might become highly proficient at mastering a video game to the point it can easily defeat human players. However, change the game, and the neural network needs to be trained all over again because it doesn’t understand context.
With an increased demand for real-time local data analysis, for example, from IoT devices, the time to quickly retrain deep learning models to understand new information may not be sufficient to keep up with the pace of data inflow.
5. Deep Learning Security
Deep learning networks have some potentially exciting applications for empowering cybersecurity. However, taking a step back to the networks themselves, and bearing in mind the propensity for outputs from these models to alter after input modifications, these networks may be vulnerable to malicious attacks.
For example, self-driving vehicles are partly powered by deep learning. If one was to access the deep learning model and make input alterations, vehicle behavior could potentially be controlled in a malicious manner. This paper highlights a black-box attack on several deep learning networks that resulted in them making misclassifications.
Closing Thoughts
While much has been written about the overwhelming array of applications and uses for artificial intelligence and deep learning, it’s important to bear in mind that the adoption of such technologies presents many challenges in addition to all the buzz and excitement.
These challenges are by no means insurmountable, and both talented data scientists and developers work tirelessly to enhance and refine the underlying models. However, a more informed perspective can only be a good thing if you are to get the most from AI and deep learning.