GE Acquires Wise.io to Accelerate Machine Learning Efforts
GE have just announced a deal that saw its GE Digital division snapping up the machine learning startup Wise.io in a deal that has been kept so far quite quiet. The company has acquired Wise.io in an attempt to strengthen its own deep learning techniques with the business. They will also be looking to have the team from Wise make improvements to their own Digital Twin services that are focused on the Internet of Things (IoT) [read more ... and more ...].
Software Engineer - Machine Learning & Artificial Intelligence
Rocket Fuel is a leading programmatic marketing platform that learns. Through artificial intelligence at big data scale, we optimize performance, awareness, and lift across channels for agencies and marketers. At over 1,000 employees strong, our bold and innovative team continues to reach new frontiers while fueling incredible success for our customers.
The machine learning team sits at the heart of our technology, and delivers on the core brand promise of bringing rocket science to make advertising more rational and effective.
What you will do:
Rocket Fuel’s technology platform supports a real-time bidding system that handles over 100 billion events every day (vs ~ 3 billion daily Google search queries), a reporting system that aggregates and analyzes terabytes of data in real-time, and a learning system that applies machine learning and artificial intelligence techniques to over 40 petabytes of data. These systems all work in harmony so that Rocket Fuel can serve the right advertisement to the right user at the right time.
The Artificial Intelligence and Machine Learning team’s work sits at the core of our technology as the key decision making agent for our systems. We research and develop the algorithms and models that make our software intelligent, as well as implementing, scaling, and maintaining the code that powers our production systems.
Some of the problems we work on include:
- Click/Conversion Prediction : How do you use the rich data we have about users to make predictions? How do you handle conversion events that are really rare, such as purchasing a cruise? How do you deal with cold start problems?
- Bidding Strategies : How do you translate a click or conversion score into a bid? What if it’s a first price auction, second price auction, or has a floor? How do you take into account pacing for hour of day, day of week, and special holidays like Thanksgiving and Christmas?
- A/B Testing : When is a result significant? How do you weight across many different campaigns?
- Fraud Detection : Many users of the web are not human, but rather bots, crawlers, or other pieces of software that is loading web pages. How do you detect these users and protect Rocket Fuel from showing ads to them?
- Recommended Systems : Rocket Fuel dynamically tailors ads to each user, including the products shown. How do you decide which products to show in just a few milliseconds?
We work with some of the most cutting edge technologies to solve these problems, including Yarn/Hadoop (close to 2000 nodes), Spark, Hive, Hbase, and Storm. We program mostly in Scala, Java, and Python.
We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. We are hiring for all levels with compensation and role depending on experience.
What is Statistical Reinforcement Learning
In an unknown environment (e.g., in a maze), a computer agent (e.g., a robot) takes an action (e.g., to walk) based on its own control policy. Then its state is updated (e.g., by moving forward) and evaluation of that action is given as a "reward" (e.g., praise, neutral, or scolding). Through such interaction with the environment, the agent is trained to achieve a certain task (e.g., getting out of the maze) without explicit guidance. A crucial advantage of reinforcement learning is its non-greedy nature. That is, the agent is trained not to improve performance in a short term (e.g., greedily approaching an exit of the maze), but to optimize the long-term achievement (e.g., successfully getting out of the maze).
A reinforcement learning problem contains various technical components such as states, actions, transitions, rewards, policies, and values.
Let us consider a maze problem, where a robot agent is located in a maze and we want to guide him to the goal without explicit supervision about which direction to go. States are positions in the maze which the robot agent can visit. In this example, actions are possible directions along which the robot agent can move [more ... ].
