Just like the way human beings can draw, paint, sing, dance, recite poems and do other creative work, there is an understanding that machines powered by some of the latest technologies could possibly do the same perfectly. Artificial Intelligence (AI) is uniquely billed as one of those emerging technologies that will power machines to just do that. But answers to questions of how truly creative these machines can be are still varied and at some extent not sufficient. There are already concerns around the integrity of tech machines; how empathetic they can be, how emotional they can get along with existing humans without offending them, and so on. There is a reality already.

The city of Amsterdam envisions a future where fleets of autonomous boats cruise its many canals to transport goods and people, collect trash, or self-assemble into floating stages and bridges. To further that vision, MIT researchers have given new capabilities to their fleet of robotic boats -- which are being developed as part of an ongoing project -- that lets them target and clasp onto each other, and keep trying if they fail. About a quarter of Amsterdam's surface area is water, with 165 canals winding alongside busy city streets. Several years ago, MIT and the Amsterdam Institute for Advanced Metropolitan Solutions (AMS Institute) teamed up on the "Roboat" project. The idea is to build a fleet of autonomous robotic boats -- rectangular hulls equipped with sensors, thrusters, microcontrollers, GPS modules, cameras, and other hardware -- that provides intelligent mobility on water to relieve congestion in the city's busy streets.

LONDON - Thousands of protesters greeted President Donald Trump's U.K. visit with anger and British irony Tuesday, crowding London's government district while the U.S. leader met Prime Minister Theresa May nearby. Feminists, environmentalists, peace activists, trade unionists and others demonstrated against the lavish royal welcome being given to a president they see as a danger to the world, chanting "Say it loud, say it clear, Donald Trump's not welcome here." "I'm very cross he's here," said guitar teacher Katie Greene, carrying a home-made sign reading "keep your grabby hands off our national treasures" under a picture of one of Queen Elizabeth II's corgis. My sign is flippant and doesn't say the things I'd really like to say." A day of protests began with the flying of a giant blimp depicting the president as an angry orange baby, which rose from the grass of central London's Parliament Square. One group came dressed in the red cloaks and bonnets of characters from Margaret Atwood's "The Handmaid's Tale," which is set in a dystopian, misogynist future America. Demonstrators filled Trafalgar Square and spilled down Whitehall, a street lined with imposing government offices, before marching half a mile to Parliament. Many paused to photograph a robotic likeness of Trump sitting on a golden toilet, cellphone in hand. The robot caught the attention of passers-by with its recitation of catchphrases including "No collusion" and "You are fake news." "It's 16 feet high, so it's as large as his ego," said Don Lessem from Philadelphia, who built the statue from foam over an iron frame and had it shipped by boat across the Atlantic. Lessem, a dinosaur expert who makes models of prehistoric creatures, said "I'm interested in things that are big, not very intelligent and have lost their place in history." "I wanted people here to know that people in America do not support Trump in the majority .

Apple is utilizing some of Siri's smarts to put an end to spam calls. The tech giant unveiled a new feature in its latest mobile software, iOS 13, called'Silence unknown callers' that should make it more difficult for spammers to reach you. Now, when a spammer calls your phone, Siri will automatically route them to voicemail. The tech giant unveiled a new feature in its latest mobile software, iOS 13, called'Silence unknown callers' that should make it more difficult for spammers to reach you The feature was debuted on Monday at Apple's annual Worldwide Developer Conference, where the firm also rolled out a new Mac Pro and Pro display, software updates for the iPhone, iPad, Mac and Watch, as well as other new features. With'Silence unknown callers,' Apple's digital assistant will scan your incoming calls for spammers and unknown numbers so that you don't have to.

People with six fingers on each hand may have trouble buying gloves, but new research shows they are better at many tasks than those with just five. Researchers found makers of robots should consider giving their creations six fingers. In a study, two people, a German mother and son, both with six fingers on each hand, were given a variety of physical tasks to carry out. They found that they could carry out many tasks, such as tying a shoelace, with just one hand, rather than two. In a study, two people, a German mother and son, both with six fingers on each hand, were given a variety of physical tasks to carry out.

Tinder is giving users more tools to express their sexuality. The dating app announced on Tuesday that users can now select up to three terms that they most identify with from a list of nine options. Tinder is giving users more tools to express their sexuality. Users can choose from nine orientations, including straight, gay, lesbian, bisexual, asexual, demisexual, pansexual, queer and questioning. From there, they can decide whether they want that information to show up on their public-facing profile.

A government watchdog has revealed that the FBI has access to about 640 million photographs -- including from driver's licenses, passports and mugshots -- that can be searched using facial recognition technology. The figure reflects how the technology is becoming an increasingly powerful law enforcement tool, but is also stirring fears about the potential for authorities to intrude on the lives of Americans. It was reported by the Government Accountability Office (GOA) at a congressional hearing in which both Democrats and Republicans raised questions about the use of the technology. The FBI maintains a database known as the Interstate Photo System of mugshots that can help federal, state and local law enforcement officials. The images include driver's licenses, passports and mugshots - prompting concerns of pivacy invasion It contains about 36 million photographs, according to Gretta Goodwin of the GAO.

We propose a novel feature coding method that exploits invariance. We consider the setting where the transformations that preserve the image contents compose a finite group of orthogonal matrices. This is the case in many image transformations such as image rotations and image flipping. We prove that the group-invariant feature vector contains sufficient discriminative information when we learn a linear classifier using convex loss minimization. From this result, we propose a novel feature modeling for principal component analysis, and k-means clustering, which are used for most feature coding methods, and global feature functions that explicitly consider the group action. Although the global feature functions are complex nonlinear functions in general, we can calculate the group action on this space easily by constructing the functions as the tensor product representations of basic representations, resulting in the explicit form of invariant feature functions. We demonstrate the effectiveness of our methods on several image datasets.

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical. The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field. Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research. Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining. In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using flexible tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost. In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and ensure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms.

The main theme of this work is a unifying algorithm, abbreviated as L2S, that can deal with (strongly) convex and nonconvex empirical risk minimization (ERM) problems. It broadens a recently developed variance reduction method known as SARAH. L2S enjoys a linear convergence rate for strongly convex problems, which also implies the last iteration of SARAH's inner loop converges linearly. For convex problems, different from SARAH, L2S can afford step and mini-batch sizes not dependent on the data size $n$, and the complexity needed to guarantee $\mathbb{E}[\|\nabla F(\mathbf{x}) \|^2] \leq \epsilon$ is ${\cal O}(n+ n/\epsilon)$. For nonconvex problems on the other hand, the complexity is ${\cal O}(n+ \sqrt{n}/\epsilon)$. Parallel to L2S there are a few side results. Leveraging an aggressive step size, D2S is proposed, which provides a more efficient alternative to L2S and SARAH-like algorithms. Specifically, D2S requires a reduced IFO complexity of ${\cal O}\big( (n+ \bar{\kappa}) \ln (1/\epsilon) \big)$ for strongly convex problems. Moreover, to avoid the tedious selection of the optimal step size, an automatic tuning scheme is developed, which obtains comparable empirical performance with SARAH using judiciously tuned step size.