AI News, heidelberg.ai artificial intelligence

Self-Supervision: Learning to Learn

major challenge of artificial intelligence is to learn models that generalize to novel data.

While training images and videos are easily available, labels are not, thus motivating self-supervised learning.

Ommer will present a widely applicable strategy based on deep reinforcement learning to improve self-supervision.

Ommer will present a variety of applications of this research ranging from behavior analysis in neuroscience to data analysis in the digital humanities.

Björn is one of the directors of the HCI and of the IWR, principle investigator in the research training group 1653 ('Spatio/Temporal Graphical Models and Applications in Image Analysis'), and a member of the executive board and scientific committee of the Heidelberg Graduate School HGS MathComp.

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions)

In today's fast-paced digital landscape, artificial intelligence can help your business create more effective marketing and social media strategies.

Alexi Venneri, best-selling business author, and CEO and co-founder of Digital Air Strike, shares some of her hard-won insights and favorite tools/software programs to help you integrate AI into your marketing workflow.

“AI does an exceptional job of collecting data about your audience, which allows you to create a precise buyer persona.” With this customer profiling process, your marketing department gains a clear understanding of a prospect's habits, spending motives and common questions.

“A MarketingSherpa case study revealed a 171% increase in marketing-generated revenue for a company using buyer personas.” Venneri’s Top Picks: Do you really know what people are saying about your brand?

Venneri says the best tools allow you to do the following: Venneri’s Top Picks: The quality of service and engagement an individual has with your company before, during, and after the sales process is pivotal.

This helps boost your visibility and also drives traffic to your website.” Venneri’s Top Picks: “All signs point to AI having an ongoing, positive influence on how businesses are perceived in the marketplace while boosting sales,” Venneri concludes.

“It's changing the way companies market their products and services.” The platform providers noted above offer social media and marketing tools that utilize AI to develop buyer personas, monitor social media channels, provide automated customer service and engagement, and optimize content.

How artificial intelligence can help us make judges less biased

As artificial intelligence moves into the courtroom, much has been written about sentencing algorithms with hidden biases.

Chen, who holds both a law degree and a doctorate in economics, has spent years collecting data on judges and US courts.

In a new working paper, Chen lays out a suggestion for how large datasets combined with artificial intelligence could help predict judges’ decisions and help us nudge them to make sentencing fairer.

We have a paper on early predictability where we used machine learning to try to predict judges’ decisions in asylum cases.

It turns out, we can make a very good prediction as to how the judge will rule before the case even opens, using only information on the judge’s identity and the nationality of the asylum seeker.

So your idea is that if we can spot which judges tend to be “predictable” — implying that they might rely more on snap judgments — we can alert them to this fact and suggest that they deliberate more carefully?

Based on what you’ve done in the past, you tend to be a little bit more biased in this direction.” In the early predictability study, you only used limited information on a judge’s identity and nationality.

More broadly, you suggest that we can use other datasets, combined with artificial intelligence, to detect instances when judicial decisions can be predicted by extraneous factors.

I’m not talking about showing them a litany of biases, which can be hard to keep track of, but about offering a theoretical framework to understand a lot of different phenomenon and all the ways and reasons we’re influenced.

I think it’s a little related to the fact that people like to think we’re unique and so being compared to someone else in this way isn’t quite recognizing my individuality and dignity.

Even if it’s based on biased data, if you have biased humans who are currently making decisions, maybe a slightly biased prediction is still slightly fairer.

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On Tuesday, September 25th, Jeff Dean, Head of Google AI and Google Brain, visited heidelberg.ai ( at the German Cancer Research ..

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Towards Motor Skill Learning | Jan Peters | heidelberg.ai

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AI Is Eating Our World | Fabian Westerheide | TEDxHeidelberg

Fabian's TEDx Talk dived into the challenges and opportunities of a world in which intelligent machines and humans coexist together. Artificial intelligence is ...

Modelling Probability Distributions using Neural Networks | Christian Baumgartner | heidelberg.ai

Heidelberg AI Talk 29th October 2018 | Modelling Probability Distributions using Neural Networks - Applications to Medical Imaging | Christian Baumgartner, ...

heidelberg.ai - Deep Generative Models (Tutorial)

This talk was part of our tutorial series "Advanced Deep Learning Methods for Medical Image Analysis". Jens Petersen introduced GANs and VAEs in this ...

4th HLF – Hot Topic: Artificial Intelligence – Presentation Dirk Helbing

Analyzing the challenges posed by Artificial Intelligence at the 4th Heidelberg Laureate Forum – Experts discuss the costs and benefits created by developments ...

Does Computational Complexity Restrict Artificial Intelligence (AI) and Machine Learning?

Sanjeev Arora (Princeton University) Simons Institute Open Lecture

4th HLF – Hot Topic: Artificial Intelligence – Presentation Thomas Dreier

Analyzing the challenges posed by Artificial Intelligence at the 4th Heidelberg Laureate Forum – Experts discuss the costs and benefits created by developments ...