Optimization has been an important factor and topic of interest in training deep learning models, yet less attention has been given to how we select the optimizers we use to train these models. Hence, there is a need to dive deeper into how we select the optimizers we use for training and the metrics that determine this selection. In this work, we compare the performance of 10 different optimizers in training a simple Multi-layer Perceptron model using a heart disease dataset from Kaggle. We set up a consistent training paradigm and evaluate the optimizers based on metrics such as convergence speed and stability. We also include some other Machine Learning Evaluation metrics such as AUC, Precision, and Recall, which are central metrics to classification problems. Our results show that there are trade-offs between convergence speed and stability, as optimizers like Adagrad and Adadelta, which are more stable, took longer time to converge. Across all our metrics, we chose RMSProp to be the most effective optimizer for this heart disease prediction task because it offered a balanced performance across key metrics. It achieved a precision of 0.765, a recall of 0.827, and an AUC of 0.841, along with faster training time. However, it was not the most stable. We recommend that, in less compute-constrained environments, this method of choosing optimizers through a thorough evaluation should be adopted to increase the scientific nature and performance in training deep learning models.

In January, Liz O'Sullivan wrote a letter to her boss at artificial intelligence startup Clarifai, asking him to set ethical limits on its Pentagon contracts. WIRED had previously revealed that the company worked on a controversial project processing drone imagery. O'Sullivan urged CEO Matthew Zeiler to pledge the company would not contribute to the development of weapons that decide for themselves whom to harm or kill. At a company meeting a few days later, O'Sullivan says, Zeiler rebuffed the plea, telling staff he saw no problems with contributing to autonomous weapons. Clarifai did not respond to a request for comment.

In January, Liz O'Sullivan wrote a letter to her boss at artificial intelligence startup Clarifai, asking him to set ethical limits on its Pentagon contracts. WIRED had previously revealed that the company worked on a controversial project processing drone imagery. O'Sullivan urged CEO Matthew Zeiler to pledge the company would not contribute to the development of weapons that decide for themselves whom to harm or kill. At a company meeting a few days later, O'Sullivan says, Zeiler rebuffed the plea, telling staff he saw no problems with contributing to autonomous weapons. Clarifai did not respond to a request for comment.

Liz O'Sullivan says she struggled for months as she learned more about the military project her in which her employer, Clarifai, was participating. Liz O'Sullivan says she struggled for months as she learned more about the military project her in which her employer, Clarifai, was participating. On the night of Jan. 16, Liz O'Sullivan sent a letter she'd been working on for weeks. It was directed at her boss, Matt Zeiler, the founder and CEO of Clarifai, a tech company. "The moment before I hit send and then afterwards, my heart, I could just feel it racing," she says.

When a news article revealed that Clarifai was working with the Pentagon and some employees questioned the ethics of building artificial intelligence that analyzed video captured by drones, the company said the project would save the lives of civilians and soldiers. "Clarifai's mission is to accelerate the progress of humanity with continually improving A.I.," read a blog post from Matt Zeiler, the company's founder and chief executive, and a prominent A.I. researcher. Later, in a news media interview, Mr. Zeiler announced a new management position that would ensure all company projects were ethically sound. As activists, researchers, and journalists voice concerns over the rise of artificial intelligence, warning against biased, deceptive and malicious applications, the companies building this technology are responding. From tech giants like Google and Microsoft to scrappy A.I. start-ups, many are creating corporate principles meant to ensure their systems are designed and deployed in an ethical way.

The biggest danger AI poses today isn't the potential of killer robots or Roko's Basilisk--it's the potential to scale bias and racism to the size of the internet. The latest example of this is an "ethnicity detection" algorithm marketed by Moscow-based NtechLab as an "upcoming feature" to the facial recognition technology it sells. The new algorithm which promises to accurately look at images of people and determine their ethnic background; an image that was on the site, but has since been removed due to public backlash, showed classifications like "European," "African," and "Arabic." While the image has been removed, ethnicity recognition is still listed as an upcoming product on the NtechLab site. Privacy advocates like the American Civil Liberties Union already decry the use of facial-recognition AI in most cases, making the case that widespread adoption of the technology would mean we would live under constant surveillance by police or large tech companies.

Two technology booms -- some people might call them frenzies -- are combining to turn a once-obscure type of microprocessor into a must-have but scarce commodity. Artificial-intelligence systems, made by companies ranging in size from Google to the Chinese startup Malong Technologies, rely heavily on a computer chip called a graphics processing unit, or GPU. The chips are also very useful in mining digital currencies like Ethereum, a bitcoin alternative riding the same wave of hype as its more famous cousin. With people and companies involved in the two surging tech niches buying up the same chips, GPUs have been in short supply over the past several months. Prices have increased by as much as 50 percent, according to some resellers and customers. "The chips are simply going out of stock," said Matt Scott, a technologist from the United States who founded Malong after leaving Microsoft's research lab in Beijing in 2014.

Artificial intelligence (AI) and machine learning are buzzwords that have entered the vernacular at many enterprises, but few have managed to realize the full benefits of the technologies. But 2018 may be the year that companies begin more strategic implementations and start realizing some of AI's benefits. "The percolation of AI and machine learning technologies into businesses still seems to be in its early stages, ranging over awareness that they need to collect data, to awareness that they already have a lot of data but are not making productive use of it, to rudimentary analyses of these data," said Pradeep Ravikumar, Associate Professor, Machine Learning Department, School of Computer Science, Carnegie Mellon University. AI will continue to be a fast-moving field in the coming year, and it's critical for companies to have close contact and collaborations with those in the AI research community to stay on the cutting edge, Ravikumar said. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) "From autonomous drones to AI-powered medical diagnostics, 2018 will see the needs of AI expand beyond research as companies bring these solutions to market," said Julie Choi, head of marketing in the Artificial Intelligence Products Group at Intel.

Even with universities now offering master's degree programs in data science (as opposed to only PhDs), that still won't produce enough pros. "You need teams of data scientists who can actually understand neural networks and tweak them," says Matthew Zeiler, who founded the visual recognition startup Clarifai in 2013 after earning his PhD in computer science. Machine learning, which digests huge amounts of data to identify patterns, is the hottest branch of AI today, with applications as diverse as organizing cell-phone photos, teaching computers to drive autonomous cars, and studying cancer. As Gilbert explains, "No matter how many [people] we train--and other companies are doing the same thing--it's just not enough to make machine-learning AI mainstream." Which is why the industry is moving toward a point-and-click AI revolution.

Armies of content moderators are working to scrub social networks of the worst content humanity has to offer -- violence, gore, hardcore sexual imagery -- and they can't keep up. The disturbing litany of murders, suicides and assaults have already become macabre technological milestones. These include Robert Godwin Sr., the 74-year-old father of nine and grandfather of 14 who was selected by a gunman at random and then murdered in a video posted to Facebook in mid-April. One week later, a man in Thailand streamed the murder of his 11-month old daughter on Facebook Live before taking his own life. The beating and torture of an 18-year-old man with intellectual and development disabilities was live-streamed on the service in January, and the tragic shooting death of two-year-old Lavontay White Jr. followed a month later on Valentine's Day.