This article was written by Blair Goldenberg, a Financial Analyst at I Know First, and enrolled in a Masters of Finance at Colorado State University.

Deep Learning Applications

All of the top tech companies want to be part of the artificial intelligence revolution so they are all investing mass amount of time and resources in order to achieved the best model. Each company has its own specific approach that sets it apart from the others in order to achieve the same end goal, the ability to utilize AI Deep Learning.

Google

Google’s DeepMind uses Reinforcement Learning as well as Deep Learning. RI is a type of machine learning in AI where the machine detects the specific ideal behavior in each situation that can maximize its performance and the machine receives feedback in the form of rewards in order to reinforce the correct output. The machine can continue learning and evolving with new reward feedback.

It can be compared to training a dog. A dog responds to treats, so in order to achieve a specific outcome, the teacher will present a treat and guide the dog through an action in which the teacher desires a certain outcome. The dog then receives a treat for completing the action, this is done over and over again until the dog no longer needs the reinforcement. Computers that use RL receive the treat in the form of feedback and the computer associates the positive feedback given as a desired result, ultimately teaching the computer to react in such a way.

Facebook

Facebook has a few uses for its AI research (FAIR). The uses are as follows:

1. Textual analysis

This may sound surprising but text is the most useful information when analyzing data on the Facebook platform. Videos and pictures can help but they aren’t as important as plain text. Facebook has developed an AI system called DeepText that analyzes words that are posted on the site as well as the context of the word in a sentence. In DeepText, the system uses neural networks to analyze relationships among words within their context in a specific sentence, compared to the word used in difference contexts. There is no reference data for DeepText because the type of learning is semi-supervised, which means that a person has to input certain data to essentially help the learning of the AI. Facebook also uses what is called “language agnostic” which assigns certain labels to words so that the system doesn’t get “confused” by spelling errors, slang, etc. The system can also switch languages, applying what it has already learned from other, previous languages analyzed.

DeepText is used on Facebook all of the time and consumers see its effects every time they log in. DeepText analyzes conversations and picks up on certain products that the users discuss, which then uses that data for targeted advertising. For example, a Facebook user either posts about a certain product, or talks about it with another user in private messages, Facebook then uses the text, analyzes it and picks up on the product name. Then, Facebook decides whether that product is appropriate for the site to market it to the user which is dependent on the context the users have given in their conversation.

2. Facial recognition

DeepFace is also used on Facebook and is another form of Deep Learning. The system helps identify people in photos that are posted on Facebook. Research has shown that its more effective than human identification, scoring 97% accuracy compared to 96% accuracy for humans. This is also an aspect of Facebook that has been observed by users. When uploading a picture, Facebook automatically adds tag suggestions based on who is in the photo, and it’s oddly accurate and surprising for a lot of users.

This has been controversial recently because the accuracy works just the same in candid crowd photographs. The reason its controversial is because it inhibits the free movement of regular people on a day to day basis because they may be recognized in a photo without their consent. However, even though there is a crude, accurate form on Facebook, the actual Deep Learning form hasn’t been released due to the EU’s ruling in 2013 that Facebook should remove it from their interface.

In my opinion, however; I believe that the DeepText is more of a problem then the DeepPhoto system because it uses everything that you’ve ever typed on Facebook to analyze users. Although it may be used for marketing currently, it can also be used for many other malicious purposes that the public may not know about, but as of now, we have no proof and it is just speculation.

3. Designing AI applications Facebook also has an AI system that tests AI systems. In other words, the system is used by engineers to help realize their ideas and build on opportunities. The system is called Flow and runs 300,000 models each month to improve existing AI functions.

Microsoft

Google and Microsoft AI is similar in that they focus on engineering which led to the discovery of AI called Residual Networks which involves layers in a network. Inputs in lower layers of the network can be accessed by layers that are higher up. Microsoft uses 152 layers in their Residual Network, which is used for image recognition.

OpenAI

Elon Musk created OpenAI because he saw how quickly other tech companies were developing their own forms of Deep Learning. OpenAI uses generative models, or generative adversarial networks (GANs). OpenAI is also investing substantially in Reinforcement Learning. Generative models can be trained by inputting enormous amounts of data and then teaching the model to generate similar data. Neural Networks are used as generative models but with less data, which then trains the generative model to create the new data.

Baidu

Image and speech processing have been Baidu’s concentration with AI research at the Institute of Deep Learning that the company opened in 2013. Other areas of research include machine learning, robotics, human-computer interaction, 3D vision and heterogeneous computing. Retrieved from Baidu Research. For a short time, BMW and Baidu worked together in the research and development of their self-driving car.

Nvidia

Earlier this year, Nvidia released their GPU-accelerated artificial intelligence computing platform. They spent $2 billion on the research and development of the AI system. Nvidia is also lucky enough to be partnered with Tesla, the electric automotive leader. Tesla recently decided to replace Mobileye (MBLY) with Nvidia’s Computer Vision for its Tesla Autopilot program. Tesla will use Nvidia’s parallel computing platform, Nvidia CUDA Deep Neural Network (cuDNN) to help develop Tesla’s true self-driving cars.

“Using cuDNN 5 of course will require Tesla to buy Nvidia’s Drive PX 2 AI supercomputer for autonomous vehicles. If Tesla aims to build 100k self-driving electric cars per year, it will need to buy 100k units of the Nvidia Drive PX 2. Tesla’s ‘Tesla Vision’ project will have to use Drive PX 2 processor/boards to take advantage of Nvidia’s DriveNet and DIGITS – a deep neural network platform that comes with 9 inception layers, 37 million neurons, 3 convolutional layers and can process 40 billion operations while offering multi-class and single object detections” says Motek Moyen Research.

The same technology that Nvidia is using for Tesla’s new self-driving cars is going to be used with Microsoft’s cloud. This means that now AI can be used by just about any business, making it increasingly accessible to the world. With the AI, businesses can provide better experiences for their customers as well as make better decisions for themselves.

Advanced Micro Devices

Advanced Micro Devices (AMD) has recently announced that they will be producing new Radeon Chip applications that will be based on AI technology, and more specifically machine learning. The chips will be based on GPU chips like Nvidia.

“Radeon Instinct is set to dramatically advance the pace of machine intelligence through an approach built on high-performance GPU accelerators, and free, open-source software in MIOpen and ROCm,” said Raja Koduri, head of AMD’s Radeon Technologies Group. “With the combination of our high-performance compute and graphics capabilities and the strength of our multi-generational roadmap, we are the only company with the GPU and x86 silicon expertise to address the broad needs of the data center and help advance the proliferation of machine intelligence.”

To read more about Radeon Chips, click here.

IBM

IBM developed a supercomputer which they refer to as Watson. Watson starred on Jeopardy in 2011 where the computer won against the two of Jeopardy’s greatest champions. Since Watson, IBM has been lacking in the AI field. They don’t invest very much in the space of Deep Learning, past the small amount used in Watson, which is outdated about 5 years later. Watson may still be the key to improved AI performance, if you’d like to read more on Watson, please click here.

I Know First

The I Know First system is a predictive stock forecast algorithm based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks and Genetic Algorithms incorporated in it.

This means the algorithm is able to create, modify, and delete relationships between different financial assets. Based on the relationships and the latest market data, the algorithm calculates its forecasts. Since the algorithm learns from its previous forecasts and is continuously adapting the relationships, it adapts quickly to changing market situations.

Conclusion

The tech industry is booming with new Deep Learning methods, each finding their own niche in the market. All of the big names in tech have to be on top of AI in order to compete with each other. I Know First takes part in the boom and has proven success within Deep Learning, Neural Networks and Genetic Algorithms.