Dear all,

We are seeking a full-time Research Associate/Research Assistant in AI and Robotics to join the Human-AI Teaming (HAT) lab at King's College London, in the Department of Informatics. The post is for up to 36 months, starting from September 1st 2019 or soon thereafter.
The salary is up to £44,015 per annum depending on experience.

More info about the post here:
https://my.corehr.com/pls/kingrecruit/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=014946

The vision of the HAT lab is that humans should work together with the AI as a team. To achieve this, we focus on new research and challenges around Safe, Trusted and Explainable AI, that will allow humans to trust what the AI systems are about to do and to interact with the AI systems to co-create solutions.

We focus on AI Planning and Machine/Reinforcement Learning, and the main application area is the control of robotics and autonomous systems, where AI is used to control (teams of) moving robots as well as to provide robots with more autonomy (for example in manufacturing, space robotics, or satellites).

HAT lab webpage: https://www.human-ai-teaming.com/

We are looking for a candidate who can drive our research agenda. You will be responsible for adapting existing as well as developing new techniques, and will be driving its applications in scenarios involving physical robots in our lab. You will also be involved in co-directing the research of PhD students and to engage with collaborators and industrial partners.

King's College London is one of the best places where this type of research can be done, also thanks to the recently funded Centre for Doctoral Training in Safe and Trusted AI (https://www.kcl.ac.uk/nms/depts/informatics/stai), that aims to make King's one of the leading institutions for the research in this space.

The candidate is expected to have a PhD (or be near completion) in AI or Robotics, together with a strong track record. Expertise on Explainable AI, AI Planning, ROS, Machine Learning, Reinforcement Learning would be a plus.

Please email me if you have any question.

Many thanks and kind regards,
Dan

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Why Should you Learn AI and Machine Learning

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Supervised Machine Learning

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Statistics makes heavy use of the normal distribution for mathematical reasons, and also because the assumption of normality is often reasonable. Many natural phenomena are normally distributed, in fact.

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I've been cracking the books myself, and some books I've found helpful include:

- Introduction to Probability (Bertsekas and Tsitsiklis)
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- Handbook of Statistical Distributions (Krishnamoorthy)
- Statistical Distributions (Forbes et al.)

Hardin and Hilbe's book in the Generalized Linear Model is an excellent overview of GLMs. In addition to hierarchical modeling and mixture models, there are many extensions such as GAM, VGAM and GAMLSS, Quantile Regression, etc. Frank Harrell's book Regression Modeling Strategies is a gold mine, IMO.

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Many statistical procedures make the assumption that the data (observations) are independent and identically distributed (i.i.d.).

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For example, employee attitudes in one company are usually more similar within that company than to attitudes in another company. Hierarchical models (aka multilevel models) can be used to account for this lack of independence in the data.

Data may be correlated across time, too. For example, sales in one week are usually similar to sales in the preceding week. Many methods have been developed to account for autocorrelated data and are widely used in many disciplines.

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I have no idea how many ways there are to conduct a regression type of analysis. Hundreds would not be an exaggeration.

✴️ @AI_Python_EN

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AI and Machine Learning Part 1 of 4

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✴️ @AI_Python_EN

Nice project by Hugging Face (https://huggingface.co/) called "Write With Transformer" where you can write anything in a Google Doc-like interface and then get text completions (GPT-2 language model) multiple times. I've just tried it out and it's really cool. Check it out! #deeplearning #machinelearning

Link: https://lnkd.in/dGHqYDa

✴️ @AI_Python_EN