With advances in machine learning and the deployments of neural networks, logistic regression-powered models are expanding their uses throughout PayPal. PayPal's deep learning system is able to filter out deceptive merchants and crack down on sales of illegal products. Kutsyy explained the machines can identify "why transactions fail, monitoring businesses more efficiently," avoiding the need to buy more hardware for problem solving. The AI Podcast is available through iTunes, DoggCatcher, Google Play Music, Overcast, PlayerFM, Podbay, Pocket Casts, PodCruncher, PodKicker, Stitcher and Soundcloud.
More specifically, I provide here high-level advice, rather than about selecting specific statistical models or algorithms, though I also discuss algorithm selection in the last section. If this is the case, an easy improvement consists of increasing value differences between adjacent homes, by boosting the importance of lot area and square footage in locations that have very homogeneous Zillow value estimates. Then for each individual home, compute an estimate based on the bin average, and other metrics such as recent sales price for neighboring homes, trend indicator for the bin in question (using time series analysis), and home features such as school rating, square footage, number of bedrooms, 2- or 3-car garage, lot area, view or not, fireplace(s), and when the home was built. With just a few (properly binned) features, a simple predictive algorithm such as HDT (Hidden Decision Trees - a combination of multiple decision trees and special regression) can work well, for homes in zipcodes (or buckets of zipcodes) with 200 homes with recent historical sales price.
NVIDIA's (NASDAQ:NVDA) graphic cards have long been favorites among hardcore gamers, but who would've thought the chipmaker's stock would explode the way it has in recent times? The share price has more than tripled in just the past year, turning NVIDIA into a near eight-bagger in just five years. Of course, there's more to its run than just graphics processors. It's more an artificial intelligence computing company today, having made huge headway in two of the hottest technology fields of our times: AI and self-driving cars. For investors looking to find the "next NVIDIA," the trick is to find a company that is sitting on a big growth opportunity, or is already tapping into a soon-to-heat-up trend, but that is still flying under Wall Street's radar.
Hedge fund Renaissance Technologies is looked upon by Wall Street with awe and envy in equal measure. Particularly, Medallion Fund, an employees only fund it runs. Bloomberg last year wrote the fund has returned more than $55 billion, making it more profitable than funds run by feted veterans such as George Soros. The Renaissance flagship fund, which will turn 30 next year, has returned more than 25% profits in most of its years of investing. Money doubles in a little more than three years at that rate.
No longer just the province of specialist sectors, agent-based computing is changing the way systems interact and how they are managed. Agent-based computing has already transformed processes such as automated financial markets trading, logistics, and industrial robotics. Now it is moving into the mainstream commercial sector as more complex systems with many different components are used by a wider range of businesses. Read about the new best practices for the ERP systems and how to tackle the growth of ERP integrations. This email address is already registered.
Can technology build a better Buffett? Nevertheless, the world has yet to see anything like a Wall Street version of Deep Blue, the artificially intelligent machine that defeated chess grand master Gary Kasparov in 1997. Today those early adopters of AI, like Fidelity Investments and Batterymarch Financial, refuse to even talk about the technology. Still, artificial intelligence has steadily improved in sophistication and quietly made itself indispensable on Wall Street. According to Andrew Lo, director of the Laboratory for Financial Engineering at MIT, every investment firm embracing a math-driven strategy uses some form of AI in its research, and Lo expects the terminology to appear again soon in promotions for retail investments like mutual funds and privately managed accounts.
Automation is happening, and it will bring substantial benefits to businesses and economies worldwide, but it won't arrive overnight. A new McKinsey Global Institute report finds realizing automation's full potential requires people and technology to work hand in hand. Recent developments in robotics, artificial intelligence, and machine learning have put us on the cusp of a new automation age. Robots and computers can not only perform a range of routine physical work activities better and more cheaply than humans, but they are also increasingly capable of accomplishing activities that include cognitive capabilities once considered too difficult to automate successfully, such as making tacit judgments, sensing emotion, or even driving. Automation will change the daily work activities of everyone, from miners and landscapers to commercial bankers, fashion designers, welders, and CEOs.
Artificial intelligence and machine learning is suddenly all the rage, and for good reason. It is the future of this, and every other industry. If you've been paying attention to the evolution of technology over the past 2.6 million years, you knew it was coming. Wherever the bulk of the effort has been shouldered by human beings, we have always sought to replace us with technology that could do the job better, faster, more efficiently and, since the invention of capital, cheaper. It began with the most basic, brute force physical tasks and has progressively involved more nuanced, cognitive processes.
In recent years, AI (artificial intelligence) researchers have finally cracked problems that they've worked on for decades, from Go to human-level speech recognition. A key piece was the ability to gather and learn on mountains of data, which pulled error rates past the success line. In short, big data has transformed AI, to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways. Some applications of blockchains to AI are mundane, like audit trails on AI models.
If popular culture is an accurate gauge of what's on the public's mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don't expect to see machines displacing knowledge workers anytime soon -- they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take.