In this notebook, we will use bathymetry data provided by NOAA to detect shipwrecks from the Shell Bank Basin area located near New York City in United States. A Bathymetric Attributed Grid (BAG) is a two-band imagery where one of the bands is elevation and the other is uncertainty (define uncertainty of elevation value). We have applied deep learning methods after pre-processing the data (which is explained in Preprocess bathymetric data) for the detection. One important step in pre-processing is applying shaded relief function provided in ArcGIS which is also used by NOAA in one of their BAG visualizations here. Shaded Relief is a 3D representation of the terrain which differentiate the shipwrecks distinctly from the background and reveals them.

In 2016, the World Economic Forum claimed we are experiencing the fourth wave of the Industrial Revolution: automation using cyber-physical systems. Key elements of this wave include machine intelligence, blockchain-based decentralized governance, and genome editing. As has been the case with previous waves, these technologies reduce the need for human labor but pose new ethical challenges, especially for artificial intelligence development companies and their clients. The purpose of this article is to review recent ideas on detecting and mitigating unwanted bias in machine learning models. We will discuss recently created guidelines around trustworthy AI, review examples of AI bias arising from both model choice and underlying societal bias, suggest business and technical practices to detect and mitigate biased AI, and discuss legal obligations as they currently exist under the GDPR and where they might develop in the future. All models are made by humans and reflect human biases.

On the basis of Technology, the Artificial Intelligence in Accounting Market is studied across Machine Learning and Deep Learning and Natural …

A deep-learning system developed at Google outperformed general pathologists for Gleason grading of prostate cancer biopsies, company researchers reported.

In the opening pages of Burn-In , an FBI agent conducts close-quarters surveillance of a suspected terrorist bomber in Washington, D.C. Simultaneously, in New Jersey, an elderly gentleman listens attentively to the enthusiastic technological prognostications of a world-famous computer scientist and mathematician from the back of a hallowed lecture hall at Princeton University. Moments later, he bludgeons the speaker to death with his cane. In this, their second novel, coauthors Peter Warren Singer and August Cole—both renowned technology and policy experts—come close to perfecting the genre of educational and informative techno-thriller. Like their first such collaboration ([ 1 ][1]), this latest entry portrays a world in which conventional aspects of domestic security and law enforcement—combating terrorism, managing protests and social upheavals, tracking a serial killer, providing a secure environment on college campuses—all occur within a transformative technological context that both enables and simultaneously disrupts these myriad objectives. As the narrative unfolds, a complex tapestry of emergent, disruptive technologies is revealed. Far from the fanciful inventions that typically populate science fiction, the systems described herein are currently available or under development for imminent deployment. The D.C. traffic congestion with which agent Lara Keegan and her partner have to contend, for example, is mostly composed of driverless vehicles, their complex operational algorithms engaged in competitive maneuvering for even the slightest comparative advantage. If the agents invoke the emergency override protocol granted to law enforcement personnel and cause the other vehicles to move aside, the surveillance drones buzzing overhead will immediately transmit this activity to the news outlets that operate them, alerting the terrorist to their presence. Keegan's field of vision, meanwhile, is networked into an operations command center via virtual reality glasses, which display real-time data on the suspect's location. These “viz glasses” continuously exchange data with other law enforcement personnel, while simultaneously performing facial scans of the surrounding crowds, subjecting each passerby to massive digital analysis. Once apprehended, despite his uncooperative silence, the suspect's identity is unmasked by a Tactical Autonomous Mobility System (TAMS), a military robot whose combat utility proved minimal and is now being tested for possible use in domestic law enforcement scenarios. Keegan, we learn, has been selected to field-test this robotic deep-learning technology system because of her prior experience managing the deployment and “force mix” of unmanned systems for the Marine Corps in Afghanistan. In technology circles, what she has been asked to undertake is known as a burn-in, a lengthy trial run of any new technological breakthrough, designed to push it to its limits of reliable functionality. The novel also contains ample instances of what the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation dub the ethical, legal, and social implications (ELSI) of technological development and diffusion. Just before his death, for example, the Princeton computer scientist boasts to his elderly guest how his use of Linux open-source software to develop complex machine-learning algorithms has made artificial intelligence (AI) universally available and affordable for every conceivable purpose. As his killer peels off an AI-designed silicon facial mask (manufactured on a 3D printer to confuse the university's AI-assisted security and surveillance system), he reveals himself to be a former DARPA engineer whose wife and son were tragically killed in a Metro crash caused by dangerous emergent behaviors in one of the scientist's AI-governed public transportation systems. This narrative thread, and many others throughout the book, illustrate what coauthor Peter Warren Singer identified in his widely acclaimed book Wired for War (published in 2009) as a key constituent of technological innovation and advance: “Anything that can go wrong, will—at the worst possible moment.” The aim of this work of fiction is not merely to engage and entertain but also to educate and inform readers about the vast array of automated and increasingly intelligent autonomous systems that are proliferating in availability and use. The authors provide detailed documentation of the actual features and current use of these systems, together with a companion educational guide to help instructors use the novel to teach about the profound depths of the robotic and AI revolution that is taking place all around us. 1. [↵][2]1. P. W. Singer, 2. A. Cole , Ghost Fleet: A Novel of the Next World War (Houghton Mifflin Harcourt, 2015). [1]: #ref-1 [2]: #xref-ref-1-1 "View reference 1 in text"

Text generation is a bridge between computational linguistics and AI that automatically generates natural language text. In deep learning, RNNs have proven to work extremely well with sequential data such as text. In this case example, I will demonstrate applying LSTMs with word embeddings to generate Hamilton lyrics. Many of the ideas came from Karpathy¹ and Bansal². All of the code can be found on my GitHub.

Nikon is offering virtual seminars on a variety of imaging topics including the latest advances in confocal and super-resolution, how deep-learning can be leveraged to extract more from your data, and high-content screening applications. Fully customized seminars are also available.

Deep learning, a subset of artificial intelligence, is already making its way into day-to-day aspects of life and business. A few years back, the technology was touted to be the futuristic concept as it differs from traditional machine learning systems. Today, deep learning is capable of self-learning and improving as it assesses large data sets. It has a large number of business applications and has the potential to revolutionize industries, emerging as the next big disruption of AI. Deep learning is typically designed to imitate the way the human brain processes data.

Migrating a codebase from an archaic programming language such as COBOL to a modern alternative like Java or C is a difficult, resource-intensive task that requires expertise in both the source and target languages. COBOL, for example, is still widely used today in mainframe systems around the world, so companies, governments, and others often must choose whether to manually translate their code bases or commit to maintaining code written in a language that dates back to the 1950s. We've developed TransCoder, an entirely self-supervised neural transcompiler system that can make code migration far easier and more efficient. Our method is the first AI system able to translate code from one programming language to another without requiring parallel data for training. We've demonstrated that TransCoder can successfully translate functions between C, Java, and Python 3. TransCoder outperforms open source and commercial rule-based translation programs.

With the recent tweet of Sharif Shameem of debuild, where he showed off a unique feature of OpenAI's GPT-3 which can generate codes, it has raised an interesting question in the programming scene. Whether this new language model released by OpenAI will kill coding or will create more productive programmers in the future. Being trained on billions of words from the internet has made GPT-3 capable of creating codes in CSS, JSX, Python etc. What's more, GPT-3 doesn't need to be trained at all for specific tasks, working on zero-shot learning, which makes it the largest model in the AI world with a strong performance on many NLP datasets allowing it to perform a range of tasks. Explaining the process, he said, "all I had to do is to re-write my two initial samples, and the GPT generated outputs in plain HTML/CSS." With GPT-3, I built a layout generator where you just describe any layout you want, and it generates the JSX code for you.