AI News, Top Machine Learning Employers

Top Machine Learning Employers

NJF Global Holdings Ltd (31 Positions in New York, NY \ San Francisco, CA \ Chicago, IL) Example Job: The main task of the group is to use state of the art Machine Learning methods in order to generate financial forecasts from large datasets.

As such, the role involves developing novel machine learning techniques and applying them to seek patterns in large, dirty and noisy data sets.

The successful candidates should be able to understand the theoretical justifications for the machine learning techniques as well as be able to create the software to apply the techniques, and where necessary develop those techniques into new areas.

Additional responsibilities include: - Data collection and analysis - Investigating new technologies, prototyping concepts, porting algorithms to Firmware, evaluating user impact, and delivering products

IBM (10 Positions in Cambridge, MA \ Denver, CO \ Yorktown Heights, NY) Example Job: Design, implement, and evaluate novel visual analytics and cognitive visualization prototypes following user-centered design principles.

Northrop Grumman (7 Positions in Monterey, CA \ Colorado Springs, CO) Example Job: Develop machine learning algorithms in support of an objective, to include designing and implementing belief networks, Hidden Markov models, Artificial Immune Systems, Gaussian Mixture Models, Decision Tress, Network models based on graph theory, Information Geometry, Genetic Algorithms, Neuromorphically inspired computing algorithms (e.g., Spiking Neural Networks) , and basic statistical analysis.

 Academic and commercial groups around the world are using GPUs to power a revolution in machine learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing.

Artificial Intelligence Will Replace Tasks, Not Jobs

There is no shortage of angst when it comes to the impact of AI on jobs.For example, a survey by Pew Research Internet findsAmericans are roughly twice as likely to express worry (72%) than enthusiasm (33%) about a future in which robots and computers are capable of doing many jobs that are currently done by humans.

Here are the rolesBrynjolfsson and his co-authors identify as top candidates for machine learning: And the jobs least likely to be shredded by AI/machine learning: Brynjolfsson and his colleagues say we're having the wrong debate when it comes to AI: insteadof pondering how jobs will be wiped out, people need to focus on 'the redesign of jobs and re-engineering of business processes.'

Dr. Irving Wladawsky-Berger, former IBM mover and shaker and now one of the most informed observers of the digital economy, provided perspective on the Brynjolfsson report, noting that some of the job activities 'are more susceptible to automation, while others require judgment, social skills and other hard-to-automate human capabilities.

To the contrary, he continues, 'automating parts of a job will often increase the productivity and quality of workers by complementing their skills with machines and computers, as well as enabling them to focus on those aspects of the job that most need their attention.'

J.P.Morgan's massive guide to machine learning and big data jobs in finance

If they don't they'll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

In future, they say machines will become increasingly prevalent over the medium term too: 'Machines have the ability to quickly analyze news feeds and tweets, process earnings statements, scrape websites, and trade on these instantaneously.'

In the long term, however, humans will retain an advantage: 'Machines will likely not do well in assessing regime changes (market turning points) and forecasts which involve interpreting more complicated human responses such as those of politicians and central bankers, understanding client positioning, or anticipating crowding,' says J.P.

If you want to survive as a human investor, this is where you will need to make your niche, Before machine learning strategies can be implemented, data scientists and quantitative researchers need to acquire and analyze the data with the aim of deriving tradable signals and insights.

They can include anything from data generated by individuals (social media posts, product reviews, search trends, etc.), to data generated by business processes (company exhaust data, commercial transaction, credit card data, etc.) and data generated by sensors (satellite image data, foot and car traffic, ship locations, etc.).

The purpose of deep learning is to use multi-layered neural networks to analyze a trend, while reinforcement learning encourages algorithms to explore and find the most profitable trading strategies.

Morgan says deep learning is particularly well suited to the pre-processing of unstructured big data sets (for instance, it can be used to count cars in satellite images, or to identify sentiment in a press release.).

Existing buy side and sell side quants with backgrounds in computer science, statistics, maths, financial engineering, econometrics and natural sciences should therefore be able to reinvent themselves.

'It is much easier for a quant researcher to change the format/size of a dataset, and employ better statistical and Machine Learning tools, than for an IT expert, silicon valley entrepreneur, or academic to learn how to design a viable trading strategy,' sayKolanovic and Krishnamacharc.

The report says that too many recruiters and hiring managers are incapable of distinguishing between an ability to talk broadly about artificial intelligence and an ability to actually design a tradeable strategy At the same time, compliance teams will need to be able to vet machine learning models and to ensure that data is properly anonymized and doesn't contain private information.

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