When we think about job displacement our minds often go to factory workers and calls centers, but as leading artificial Intelligence expert Dr. Vivienne Ming points out in a recent interview with the Financial Times, the middle class in professional services may be the most challenged in this computed future. Ming cites a recent competition at Columbia University between human lawyers and AI counterparts reviewing agreements with loopholes. The AI found 95 per cent of them in 22 seconds, it took the humans over an hour. As a lawyer reading this you may have two conflicting emotions: concern, but the other should be joy that your life is about to get easier; free of the day to day drudgery of reviewing agreements, allowing you to concentrate on what really matters. This is what technology does – an advantage to highly complementary skills.

You could work for Amazon in your PJs. Amazon has more than 1,200 Amazon staff in the greater Boston area. BOSTON – Amazon isn't just a Seattle company anymore, and a visit to its offices in this university city explains why. Here, in an old Necco wafer candy factory in the formerly industrial neighborhood of Fort Point, Rohit Prasad oversees 1,200 workers developing Alexa, the company's digital assistant. Walls made out of shipping containers, a playful nod to Amazon's main business, and exposed brick echo the urban tech vibe of its Seattle headquarters.

Acoustic sensors, big data, machine learning and protection of threatened animal species: all these elements go hand in hand in the Elephant Listening Project that is taking place in the jungles of the Republic of the Congo. For 24 hours a day, the sensors collect large amounts of data from the acoustic environment of the Nouabalé-Ndoki National Park and neighboring forest areas. Among these data, the sounds of African elephants stand out, whose population has dropped by 30% in just 6 years, mainly due to poaching. But … what is the use of analyzing the sounds of elephants? Well to calculate the variations of its threatened population; but for this it is necessary to identify the sounds produced by the elephants from the rest of the jungle sounds, and then identify the elephants individually in order to count them, an impossible task to be performed from the air . This is possible thanks to Conservation Meetrics, a project promoted by Microsoft within its AI for Earth initiative, based on the use of machine learning to monitor wildlife and evaluate the results of conservation work.

As a result, scientist and biologists can track real-time movement patterns of wild elephants, alerting forest authorities of any poaching related activity (by flagging off gunshot sounds) and finding and blocking online ads that attempt to sell illegal ivory or elephant parts -- again thanks to image recognition based artificial intelligence which hunts down these online ads. Hopefully, due to this AI technique, wild elephants will start flourishing again.

We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.

Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomics and neuroscience to economics and finance. As data are collected at an ever-growing scale, statistical machine learning faces some new challenges: high dimensionality, strong dependence among observed variables, heavy-tailed variables and heterogeneity. High-dimensional robust factor analysis serves as a powerful toolkit to conquer these challenges. This paper gives a selective overview on recent advance on high-dimensional factor models and their applications to statistics including Factor-Adjusted Robust Model selection (FarmSelect) and Factor-Adjusted Robust Multiple testing (FarmTest). We show that classical methods, especially principal component analysis (PCA), can be tailored to many new problems and provide powerful tools for statistical estimation and inference. We highlight PCA and its connections to matrix perturbation theory, robust statistics, random projection, false discovery rate, etc., and illustrate through several applications how insights from these fields yield solutions to modern challenges. We also present far-reaching connections between factor models and popular statistical learning problems, including network analysis and low-rank matrix recovery.

Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities. Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions). In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion. Cross-modal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages. Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks. The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition. We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.

In the last few years, machine learning applications have quietly entered every aspect of life: social media to speech recognition, radiology to retail, warfare to writing articles, coding to customer service, robotics to route optimization. During the 40 year information age, we told computers what to do. With advances in artificial intelligence, particularly machine learning, and faster processing chips we can feed computers giant data sets and they can (in narrow slivers) draw some inferences on their own. As we reported in Ask About AI, the rise of code that learns marks the beginning of a new era of augmented intelligence. It's a great opportunity for us to expand access to a great education and for young people to make a big contribution.

Our survey mission has reached the outer perimeter of Galaxy W2246-0526. We rode our bubble of space through 12 and a half billion light years in 7.2 seconds. Took a bit longer than calculated. Traces of minor chemical adjustments in the organic passengers have occurred which are being monitored. Unease is apparent within the arrival pod as we await inflation of Habitat One. Some, like Judge Bulbous, have been pulled away from political turmoil back home only to be riding in close quarters with political rivals who are on this journey. "Watch Csaynik take credit for anything we find out here…" chuckled Senator Pretyman moments after we got here. She's one of the more vocal organics who can't accept that President Csaynik is the first "artificially" intelligent president of the U.S. ever elected. I would caution that more trouble is likely to come from the ego-driven nature of our organic crew members than from a weakness in their physiology. And in so cautioning, I will restate that which I said before we left the Milky Way; intergalactic voyages should be conducted by self-repairing artintels* like myself and our mission Alpha. Hard to believe this galaxy was once the most luminous galaxy known, putting out 10,000 times the energy of the Milky Way. The perpetual energy generation that we've come to observe seems completely self-contained near the core of the galaxy. One might say "disturbingly" dark. My examination of Alpha reveals tampering from outside. Concerned, being the only other artintel aboard Resolution, I run the same diagnostic on myself. I, too, have been compromised. Playback reveals the most likely perpetrator, who infected me and Alpha by touching our skin and releasing microlegions to drill into us, interfering with cognitive and motor functions. Senator Pretyman is arrested and an autoguard activated at Detainment. What made that organic conservative think it was okay to inject updates into artintels? Human power over sentient life was abolished with the 47th Amendment. By the time the home world's finished with her, Ms. Pretyman won't even be allowed to vote! I've given Ms. Pretyman a drink from her own subversive swamp in the form of an injected immune-weakening microbe that will leave her susceptible to ailments of all variety. Tragically, it might even cause her demise. Unlike organic physicians, I've not taken an oath to do no harm. The organics say that an oath taken by one of us is no better than an oath taken by a parrot. I can do all the harm I please. I can't find a way to save Mrs. Ichnida. She's gone into a sort of coma. However Alpha and I should be good for several days beyond their deaths.

Increasingly, it's being applied to conservation; a coalition of researchers earlier this year developed a machine learning algorithm that can identify and describe wildlife. And in a blog post this week, Microsoft highlighted a Santa Cruz-based startup -- Conservation Metrics -- that's leveraging AI to keep tabs on African savanna elephants. Conservation Metrics, a recipient of Microsoft's AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology's Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo. Its system isolates elephant calls -- the low-frequency rumbling sounds they use to communicate with one another -- from the recordings and derives insights, like population size and herd movement. It's precise enough to identify individual animals that can't be seen from the air, according to Conservation Metrics CEO Matthew McKown.