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Talking Robotics' seminars of January – April 2021 (with videos and even a musical summary!)

Robohub

Talking Robotics is a series of virtual seminars about Robotics and its interaction with other relevant fields, such as Artificial Intelligence, Machine Learning, Design Research, Human-Robot Interaction, among others. They aim to promote reflections, dialogues, and a place to network. In this seminars compilation, we bring you 7 talks (and a half?) from current roboticists for your enjoyment. Filipa Correia received a M.Sc. in Computer Science from University of Lisbon, Portugal, 2015. She is currently a junior researcher at GAIPSLab and she is pursuing a Ph.D. on Human-Robot Interaction at University of Lisbon, Portugal.


How AI Could Set Us Free

#artificialintelligence

But these scenarios depend upon an unanswered question: are machines intelligent to begin with? Computers are essentially logic machines that process digital information. But in a recent paper entitled "The Emperor of Strong AI Has No Clothes," physicist Robert K. Logan in Toronto and Adriana Braga in Rio de Janeiro argue that the dream of a super intelligence has limits that its adherents choose to ignore. The things the Singularity will never get right amount to a long list, to quote the two researchers: "… curiosity, imagination, intuition, emotions, passion, desires, pleasure, aesthetics, joy, purpose, objectives, goals, telos, values, morality, experience, wisdom, judgment, and even humor." A clever programmer can figure out how to get a computer to answer human questions like "How is your mother feeling?", "What does chocolate taste like?", and "Don't you just love fresh snow?"


Exemplars can Reciprocate Principal Components

arXiv.org Artificial Intelligence

This paper presents a clustering algorithm that is an extension of the Category Trees algorithm. Category Trees is a clustering method that creates tree structures that branch on category type and not feature. The development in this paper is to consider a secondary order of clustering that is not the category to which the data row belongs, but the tree, representing a single classifier, that it is eventually clustered with. Each tree branches to store subsets of other categories, but the rows in those subsets may also be related. This paper is therefore concerned with looking at that second level of clustering between the other category subsets, to try to determine if there is any consistency over it. It is argued that Principal Components may be a related and reciprocal type of structure, and there is an even bigger question about the relation between exemplars and principal components, in general. The theory is demonstrated using the Portugal Forest Fires dataset as a case study. The distributed nature of that dataset can artificially create the tree categories and the output criterion can also be determined in an automatic and arbitrary way, leading to a flexible and dynamic clustering mechanism.


Portugal to focus on adopting first EU artificial intelligence law - minister

#artificialintelligence

Under the Portuguese chairmanship of the Council of the European Union (EU), Portugal will focus on adopting the first EU law on artificial intelligence, based on transparency and respect for users' rights, and also expects cooperation with the US administration. "We attach great importance to the legal framework for artificial intelligence. It is now clear that artificial intelligence is the basis for enhanced productivity and has great potential for growth," said Pedro Siza Vieira, the economy minister. Speaking via videoconference in the European Parliament's Internal Market and Consumer Protection Committee on the priorities of the Portuguese presidency of the Council of the EU, he stressed that "the standards of society and individuals should be respected in the area of artificial intelligence and the algorithms involved. For this reason, Portugal will focus on adopting this first legal framework at EU level for artificial intelligence, which should be based on a "transparent framework, taking into account the risks involved and protecting the EU's values, on issues such as human rights and privacy, among others", he said.


#324: Embodied Interactions: from Robotics to Dance, with Kim Baraka

Robohub

In this episode, our interviewer Lauren Klein speaks with Kim Baraka about his PhD research to enable robots to engage in social interactions, including interactions with children with Autism Spectrum Disorder. Baraka discusses how robots can plan their actions across multiple modalities when interacting with humans, and how models from psychology can inform this process. He also tells us about his passion for dance, and how dance may serve as a testbed for embodied intelligence within Human-Robot Interaction. Kim Baraka is a postdoctoral researcher in the Socially Intelligent Machines Lab at the University of Texas at Austin, and an upcoming Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, where he will be part of the Social Artificial Intelligence Group. Baraka recently graduated with a dual PhD in Robotics from Carnegie Mellon University (CMU) in Pittsburgh, USA, and the Instituto Superior Técnico (IST) in Lisbon, Portugal.


Machine-learning software competes with human experts to optimise organic reactions

#artificialintelligence

A free software tool that can find the best conditions for organic synthesis reactions often does as well as expert chemists – somewhat to the surprise of the researchers. The software, called LabMate.ML, suggests a random set of initial conditions – such as the temperature, the amount of solvent and the reaction time – for a specific reaction, with the aim of optimising its yield. After those initial reactions are carried out by a human chemist, their resulting yields are read with nuclear magnetic resonance and infrared spectroscopy, digitised into binary code and then fed back into the software. LabMate.ML then uses a machine-learning algorithm to make decisions about the yields, and then recommends further sets of conditions to try. Researcher Tiago Rodrigues of the University of Lisbon says LabMate.ML usually takes between 10 and 20 iterations to find the greatest yield, while the number of initial reactions varies between five and 10, depending on how many conditions are being optimised.


Brutalist AI-generated buildings feature in hypnotic Moullinex music videos

#artificialintelligence

Lisbon musician Moullinex has shared an exclusive short music video showing an endlessly changing landscape of brutalist buildings drawn up by a generative design algorithm with Dezeen. Moullinex, whose real name is Luís Clara Gomes, created two videos that use artificial intelligence (AI) to imagine a series of brutalist buildings. The first video, which the artist shared on his Facebook page, is based on 200 photographs of modernist, concrete buildings. These images acted as the dataset, which was used to train a generative network via the machine learning tool StyleGAN2, to create a string of entirely non-existent buildings with similar characteristics. "It's akin to showing thousands of pictures of a cat to a child and then asking them to draw a brand new cat based on what they now know are cat-like characteristics," Gomes told Dezeen.


Portugal's Faber reaches $24.3M for its second fund aimed at data-driven startups from Iberia – TechCrunch

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Portuguese VC Faber has hit the first close of its Faber Tech II fund at €20.5 million ($24.3 million). The fund will focus on early-stage data-driven startups starting from Southern Europe and the Iberian peninsula, with the aim of reaching a final close of €30 million in the coming months. The fund is backed by European Investment Fund (EIF) and the local Financial Development Institution (IFD), with a joint commitment of €15 million (backed by the Investment Plan for Europe – the Juncker Plan and through the Portugal Tech program), alongside other private institutional and individual investors. Alexandre Barbosa, Faber's Managing Partner, said "The success of the first close of our new fund allows us to foresee a growth in the demand for this type of investment, as we believe digital transformation through Intelligence Artificial, Machine Learning and data science are increasingly relevant for companies and their businesses, and we think Southern Europe will be the launchpad of a growing number." Faber has already'warehoused' three initial investments.


Machine Learning in Smart Mobility – simusafe

#artificialintelligence

The Workshop on Machine Learning in Smart Mobility (MLSM), is co-located with the 21st International Conference on Intelligent Data Engineering and Automated Learning -- IDEAL 2020, to take place in Guimarães, Portugal, on November 4-6. The workshop's technical program will include a session hosted by the H2020 SIMUSAFE project, "New Training Modules to Increase Usage of'Soft' Modes of Transport". The workshop will gather both the ML community and transportation practitioners to discuss how cutting-edge ML technologies can be effectively applied to improve the performance of transportation and mobility systems on a sustainable basis, according to three important dimensions: economic, environmental, and social. This forum also aims to generate new ideas towards building innovative applications of machine learning into smarter, greener, and safer mobility systems, stimulating contributions that emphasize on how theory and practice are effectively coupled to solve real-life problems in contemporary transportation, naturally including all sorts of mobility modes and their intrinsic interactions. Indeed, contemporary transportation is evolving rapidly on a more intelligent basis, and the concept of Intelligent Transportation Systems (ITS) has become already a reality among us, supporting the infrastructure leading to the emergence of the so-called Smart Mobility, and to a whole bunch of Mobility-as-a-Service (MaaS) options as we witness today.


How I became a Software Developer during the pandemic without a degree or a bootcamp

#artificialintelligence

In 2018 I was depressed and unmotivated, I thought of myself as a failure and I thought I was too dumb to finish my degree or learn anything at all, I had no direction in life and just wanted everything to be over. Two years later, one spent working abroad and another dedicated to studying, I have a completely different perspective about myself and I just started my new exciting developer job on Monday. It took a lot of courage (and argumentations to convince my parents) to leave my university after three years of studies to accept a job in a Lisbon without knowing anyone nor the language but it was a wonderful experience that helped me find myself. Again it took even more grit and determination to leave Lisbon and start studying again, but I did it because I knew my dream was to become a programmer. I have no expertise in psychology and the best advice I have if you are in a dark place is to seek professional help, but I know what it feels to be lost and I want to help anyone that shares my same dream by writing this article offering actionable advice on how to achieve a career in software development.