Five Important AI and Deep Learning Challenges for 2019

Five Important AI and Deep Learning Challenges for 2019

Five Important AI and Deep Learning Challenges for 2019

As we all know, Artificial Intelligence (AI) and deep learning are hot topics, and many media speculation relates to the fact that whether AI will slowly-slowly replace human labor or it will help to increase more opportunities. However, after research, the number of jobs requiring AI skills has been increased by 450% since 2013 and this value is increasing at a tremendous rate. While AI is not really a new concept, its reach and progress in computer science have brought it to the fore in recent days.

Let's take a look a what are the five important AI and Deep Learning Challenges for 2019

1. AI and Expectations

There is a great discrepancy between actual potential uses of AI and the layman's expectations of AI technologies. The perception that the media is reporting is one of the super-smart computers with cognitive abilities which will ultimately change many jobs which humans usually do.

However, the computer and data science industries have a challenge on their hands to defined these lofty expectations by accurately conveying that AI is a tool that will increase productivity as opposed to the entire human roles. Automation of mundane tasks, data-driven predictions, and optimizations are all things which Artificial Intelligence can do very well. However, in most instances, it can not replace what the human brain brings to the table, especially in highly specialized roles.

2. Deep Learning doesn't understand context very well

The word "deep" in deep learning is more relevant to its architecture than the level of understanding that these algorithms are currently able to produce. For example, a deep learning algorithm might be able to master a video game, where it can easily beat human players. However, as the game will be changed, again there is a need to train the neural network because it does not understand the context.

With an increased demand for real-time local data analysis, from IoT devices, the time required to quickly retrain deep learning models to understand new information is not enough with this speed of data inflow.

3. Deep Learning needs enough quality Data

Deep learning works best when it has a lot of quality data available, and this performance increases as the data grow. However, when sufficient quality data is not fed in a deep learning system, it can fail quite badly.

As per the reports, in 2017 a practical was done with google deep learning system. Researchers have fooled Google's deep learning systems to make errors after adding "noise" to the available data. This was done to check whether this system works properly or not. With the small input variations in the data quality having such dangerous results on output and predictions, there is a real need to ensure greater stability and accuracy in deep learning. In addition, in certain industries, such as industrial applications, adequate data may not be available, which limits the adoption of deep learning.

4. Deep Learning Security

There are some exciting applications for empowering cybersecurity in Deep Learning Network. However, taking one step back on the network itself, and keeping in mind the trend of change for output from these models after the input modifications, these networks can be vulnerable to malicious attacks.

For example, self-driving vehicles are partly driven by deep learning. If a person uses the Deep Learning model and makes some input changes, vehicle behavior can possibly be controlled in a malicious manner. This will expose a black box attack on many deep learning networks, which will result in misclassification.

5. Making Production Ready

With the latest data showing 80% of the enterprises investing in AI, the pressure on organizations and their developers will be increased, transiting from modeling to issue production-grade AI solutions. After all, important investments in AI need to be used in solving real-life problems if they are considered worthwhile.

The focus will be on "operationalizing" AI capabilities through advanced technology infrastructure, addressing safety concerns such as informational integrity and security of AI platforms, and ensuring high availability of AI solutions so that they can distribute results, forecasts, etc. as per requirement. 

A key goal of AI in the enterprise for machines is to help the officials and important stakeholders in taking critical decisions; Both tactical and strategic wise.

For more updates on Artificial Intelligence stay tuned with us on LunaticAI.

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