Discussions of artificial intelligence usually bring up one of three scenarios in people’s minds:
Machines will destroy us, serve us, or replace us. Those are the three dominant ways society imagines humanity relating to artificial intelligence.
The Terminator and Jetsons scenarios have been the most widely discussed in fiction and entertainment (hence their names), but it’s the automation scenario that now dominates contemporary thinking around AI. Every day brings another breathless prediction that robots will take all our jobs. One of the most prominent advocates of this position is Tesla (TSLA) CEO Elon Musk, who told the National Governors Association:
“There certainly will be job disruption. Because what’s going to happen is robots will be able to do everything better than us. … I mean all of us.”
When Musk says “robots will be able to do everything better than us,” he makes two key errors. He overestimates robots, and he underestimates humans. AI will not replace humans, and it won’t destroy us or be totally subservient to us either. Instead, humans and AI will work together , combining each other’s strengths and compensating for each other’s weaknesses to create jobs and achieve results that are presently unimaginable.
The Limitations of AI
As we discussed in “Cutting Through the Smoke and Mirrors of AI on Wall Street” (and elaborated on in the second and third articles of this series), AI has a long way to go before it can compete with human intelligence. Machines may be able to defeat humans in games like Chess and Go, but they can’t compete with humans in areas such as logical intuition, much less creativity and innovation. Machines are savants: incredibly skilled at specific tasks but limited in their overall cognition.
The idea that AI will replace all human workers ignores the complexity and variability of most jobs. Sure, you can train a machine to do x, but most jobs require people to seamlessly juggle tasks x, y, and z, often at the same time and in an open environment that is not immutably structured (i.e. a chess board).
Even if AI does advance to match humanity’s level of general intelligence, it will still need to learn how to apply that intelligence. Smart machines need even smarter teachers, and humans are the best teachers around.
Figure 1: Teaching Machines
Machines are undoubtedly superior to humans at a large number of confined or routine tasks, but they will never “be able to do everything better than us.”
Machines Can Empower Us & Make Us More Human
No one understands the pain of being surpassed by a machine better than Gary Kasparov. When the reigning world chess champion was defeated by IBM (IBM) supercomputer Deep Blue in 1997, Newsweek described it as “The Brain’s Last Stand.”
Today, Kasparov sees his defeat differently. He believes the advancement of artificial intelligence represents a boon to humanity, even if machines do cause some disruption to the labor market. The more that AI replaces routine jobs, the more it frees up humans to dream up jobs that could never have existed in the past, as he wrote in an essay last year in the Wall Street Journal.
“Machines that replace physical labor have allowed us to focus more on what makes us human: our minds. Intelligent machines will continue that process, taking over the more menial aspects of cognition and elevating our mental lives toward creativity, curiosity, beauty and joy. These are what truly make us human, not any particular activity or skill like swinging a hammer—or even playing chess.”
Think of the jobs that exist today that would have been unimaginable 20 years ago. Now think of the jobs that will exist 20 years from now. Just like it would be hard to explain the concept of a social media manager to someone from 1998, so too is it difficult to comprehend the jobs that will exist in 2038. As Box (BOX) CEO Aaron Levie explained on Twitter:
“AI can seem dystopian because it’s easier to describe existing jobs disappearing than to imagine industries that never existed appearing.”
Humans are wired to fear the unknown. The loss of jobs that already exist to machines frightens us more than the potential jobs those machines will create, even though the new jobs will make use of our talents in ways that are more rewarding and productive.
How We Can Work with Machines
The cycle above—machines taking over human jobs, which then frees those humans to create new jobs—has existed for centuries. Agricultural advancements allowed farmers to move to cities and become artisans and merchants. The Industrial Revolution displaced those artisans and created jobs building railroads and steamships. Throughout history we have made technological advancements that replaced some jobs and created new ones.
What’s different about AI is our relationship to the technology. AI is not a tool where the human is in complete control. AI gets some autonomy. It acts in ways that it’s not explicitly told to and makes recommendations that humans might not anticipate or fully be able to understand. People don’t just need to learn how to use AI, they need to learn how to work cooperatively with it.
Many large financial firms have already shown an understanding that AI is best used as a complement to human labor, not a replacement for it. When BlackRock (BLK) announced last year that it would rely more heavily on algorithms to pick stocks for its funds, much of the news coverage interpreted the move as machines replacing traditional fund managers.
BlackRock, however, insists that’s not the case. Cofounder Rob Kapito outlined the company’s view on AI at the Barclays New Frontier Conference last November:
“It’s not going to replace humans. I believe it will be human and machines.”
The numbers bear Kapito’s statement out. Even though the company’s shift to more algorithmic funds led to 36 employees leaving, the firm has actually added nearly 700 employees (5% increase) over the past year. It’s not just tech talent either; BlackRock has placed an emphasis on hiring liberal arts majors. As technology changes the finance industry, employers need to start looking for different skillsets.
Research shows that cooperation between humans and machines thrives when humans leverage our uniquely human skills: intuition, pattern recognition, and reading implicit signals. Relationships thrive when both parties have a clear and early understanding of what to expect from each other.
AI has the potential to make us more human, not less, when developed with these goals:
These steps can help companies reframe AI as way to make humans more productive rather than as a way to replace them. In the words of famed investor Paul Tudor Jones:
“No man is better than a machine, and no machine is better than a man with a machine.”
This article is the fourth in a five-part series on the role of AI in finance. The first, “Cutting Through the Smoke and Mirrors of AI on Wall Street” highlights the shortcomings of current AI in finance. The second, “Opening the Black Box: Why AI Needs to Be Transparent” focuses on how transparency is crucial to both developers and users of AI. The third, “AI Has a Big (Data) Problem” details the difficulty machines have in reading large amounts of unstructured data. The final article will focus on how AI can lead to significant benefits for both financial firms and their customers.
Disclosure: David Trainer and Sam McBride receive no compensation to write about any specific stock, sector, style, or theme.
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In this post Kris will continue with the coding and implementation of a Perceptron from Scratch
Let’s now ask our perceptron to learn a slightly more difficult problem. Using the same iris data set, this time we remove the setosa species and train a perceptron to classify virginica and versicolor on the basis of their petal lengths and petal widths. When we plot these species in their feature space, we get this:
This looks like a slightly more difficult problem, as this time, the difference between the two classifications is not as clear cut. Let’s see how our perceptron performs on this data set.
We’re also going to introduce the concept of the, which is important to understand if you decide to pursue neural networks beyond the perceptron. The learning rate controls the speed with which weights are adjusted during training. We simply scale the adjustment by the learning rate: a high learning rate means that weights are subject to bigger adjustments. Sometimes this is a good thing, for example, when the weights are far from their optimal values. But sometimes this can cause the weights to oscillate back and forth between two high-error states without ever finding a better solution. In that case, a smaller learning rate is desirable, which can be thought of as fine tuning of the weights.
Finding the best learning rate is largely a trial and error process, but a useful approach is to reduce the learning rate as training proceeds. In the example below, we do that by scaling the learning rate by the inverse of the epoch number.
Here’s a plot of our error rate after training in this manner for 400 epochs:
You can see that training proceeds much less smoothly and takes a lot longer than last time, which is a consequence of the classification problem being more difficult. Also note that the error rate is never reduced to zero, that is, the perceptron is never able to perfectly classify this data set. Here’s a plot of the decision boundary, which demonstrates where the perceptron makes the wrong predictions:
Here’s the code for this perceptron:
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With predictions claiming that the global UAV drones market could reach up to USD 127 billion by 2020, it is clear that the drones industry is tapping into the world’s demand for more data—at faster speeds and greater precision. While many still associate drone technology with the military market, which indeed accounted for almost three-quarters of the US market total in 2015, demand for commercial drone technology has been slowly catching up in the last decade, with a predicted market size of USD 1.05 billion by 2020 and expected growth at a CAGR of 57%.1
Established and Emerging Markets
The agriculture sector is by far the largest contributor to the recent surge in the commercial drones market, making up 76% of the revenue generated by the commercial UAV industry in 2015. In precision farming, UAVs assist by apportioning pesticides and fertilizers to only areas that require them, resulting in significant cost savings. Technavio’s market research study adds that in agriculture, “a $300 UAV can be used to check for disease and irrigation needs, while the same task performed with manned aircraft will cost at least $1,000 per hour.”2
The insurance industry is one of the latest markets to tap into the potential of drone aerial data collection. According to Cognizant, UAVs can improve productivity by up to 50%, by helping adjustors assess damage in difficult to reach areas both at the time of binding the account and after a claim is made.
Compared to satellite technology, which has been amassing valuable data for at least half a century, modern drones have been on the aerial data collection scene for only about two decades. Indeed, prior to 2005, the Federal Aviation Administration (FAA) barely differentiated between drones and model aircrafts flown by hobbyists.
But in the last several years the FAA has come a long way, developing sensible regulations that will make way for more widespread industry adoption of UAVs. This according to Murray Wu, CEO of Reforges, a Toronto-based startup offering drone fleets for hire to commercial businesses.
Much as the boom in satellite technology has provided stakeholders with actionable data insights, drones appear poised to do the same.
Unlike satellites, however, which can be finicky, limited by slow imagery refresh rates, (and expensive to purchase, maintain, and upgrade) drones are able to capture data in non-ideal conditions, such as through rain, clouds, darkness, or even concrete walls. They are versatile, modular, and relatively inexpensive compared to other aircraft, unmanned and otherwise.
This is not to say that drones will replace satellites altogether. In many ways, UAVs can be described as micro-satellites, collecting data on a smaller scale and with greater agility. Many strategies still benefit from the macro-perspective that satellite data provides.
Some also point to the flaws in current drone technology – especially as they relate to accurate data gathering. Jonathan Rupprecht is a lawyer who specializes in drones, and a pilot. He authored Drones: Their Many Civilian Uses and the U.S. Laws Surrounding Them. He says that the drone industry needs to communicate more with the aviation industry, because they have figured out things
like aerodynamic efficiency, which would mean better flight times for drones, and GPS, which would reduce drone “fly-aways” due to errant GPS signals. Rupprecht also points out that drones use LIDAR, a laser range finder that doesn’t have “very accurate or calibrated inertia measuring units (IMU) to measure changes in movement.” This can be somewhat addressed by using cameras, but the concern remains that investors could be trading on inaccurate data.
Still, commodities traders like Anurag Bhatia from Minance Capital claim to already be using drones to predict agricultural commodity prices such as wheat, cotton, sugar and maize. It’s undoubtedly early, but where there’s a will, there’s a way.
From Hardware to Software
The drone market can be broken into four major product types: 1) the UAV itself, which includes large and small fixed-wing aircrafts, rotorcrafts, hybrid and other vehicle methods of lift, such as lighter-than-air; 2) payloads, such as cameras and other sensors, as well as other separately sold parts; 3) ground stations and controls; and 4) software/data analytics, which focuses on the collection, storage, and processing of data. It is this last category, of course, that holds promise for alpha generation.
Today, more and more companies are moving to the data processing end of the industry, introducing software to help operators process and manage the data that drones collect. Ciara Bracken-Roche, a PhD candidate at Queen’s University and a researcher in drone technologies and data collection, describes the shift: “Ten years ago, the main focus of many UAV developers and operators was to get the technology to where it needed to be: trying to reduce the size of the UAV; improving the payload; and making drones more autonomous with capabilities like sense-and-avoid. In the last couple of years, there has been a move towards data collection and analytics optimization.” Here are several key players that have been making an impact in the world of UAV data analytics:
The UK-based Sky-Futures was one of the first drone firms to receive money from Airware’s Commercial Drone Fund. Sky-Futures focuses on oil and gas inspection services (onshore and offshore) and its cloud application, the Sky Futures Inspection Portal, hosts the HD video, still and thermal imagery that its drones produce during inspections.
The France-based RedBird, a pioneer in drone data analytics for mining, quarrying, and construction industries was founded in 2013. Its cloud platform Cardinal, optimizes resources and ensures safety. Like Sky-Futures, RedBird was one of the first recipients of Airware’s Commercial Drone Fund. In FY2014-2015, the company received $3.19 million funding from three different investors and partnered with Caterpillar, a leading manufacturer of construction and mining equipment.
In September 2016, Airware announced the acquisition of Redbird. “Construction and excavation sites … must be managed and operated digitally in order to compete in today’s markets. Commercial drone technology brings these operations into the digital world while offering faster, cheaper and higher-quality data and analytics,” said Jonathan Downey, founder & CEO of Airware. “By … incorporating Redbird’s powerful analytics tools on top of the Airware platform, we are helping enterprises make this transition into the digital age.”
Gamaya, a Swiss data analytics startup that currently focuses on the soybean, corn, and sugarcane industries, recently raised $3.2 million in a Series A financing round, with the lofty goal of mitigating food scarcity through sustainable agricultural processes. The investors that participated in the round include chairman and former CEO of Nestlé, Peter Brabeck-Letmathe; the Sandoz Foundation; and VI Partners, a venture capital firm.
Gamaya uses its patented hyperspectral imaging for precision farming applications. The data captured by its drones includes alerts for disease, pests, and yield predictions. The imaging technology is so sophisticated that it can detect what is invisible to the human eye and then translate the data into action maps and recommendations for farmers. “Our future is dependent on whether we will be able to leverage technology and innovation in order to dramatically improve the efficiency of our food production methodology,” says Gamaya CEO Yosef Akhtman. “Our solution helps to increase production efficiency by providing farmers with full situational awareness of their farmland and crops.”
Kespry, a Silicon Valley startup, builds commercial drone systems that allow its clients to capture, view, and analyze aerial imagery and survey data. The company recently partnered with NVIDIA on a deep-learning module to advance its drone system’s autonomous features. With the resulting Jetson TX1 supercomputing module, Kespry’s drone systems can now analyze data in real time. In June 2016, Kespry announced the close of a Series B equity-financing round of $16 million.
Drone Data as a Source of Alpha: It’s Early
The sky really is the limit when it comes to the potential for drone data usage. There is no telling what industry UAVs will revolutionize next. Consequently, it is no surprise to hear that there is a lot of interest in the idea of selling data to investors and hedge funds. One of Reforges’s investors, for instance, is a subsidiary of a big financial institution that is looking to eventually sell drone data to capital markets. Many drone manufacturers jump at the idea of monetizing the data captured by their UAVs. If there’s a market for their data, why not sell it?
Companies like Kespry, however, are not so sure. “The industry is still at an embryonic stage,” remarks VP Marketing at Kespry, David Shearer, “Our focus is more on providing our clients with what they want: a vertically integrated service that focuses on field to finish.” Many UAV data companies worry that the selling of data could compromise the trust and relationship that is necessary for the client-building phase.
Ultimately, there is no real consensus on who owns the data that UAVs collect: the clients who commission the data or the drone companies who collect it or the property owners whose area is being observed. Lawyer Rupprecht points out that the laws for data collection differ by state. In Florida, for example, you could get sued for flying a drone over a private property and gathering data that could not have been observed on the ground at eye level. So much for orange futures.
Consequently, if drone companies want to sell the data that they have collected on behalf of their clients, they may have to reserve the rights during the initial negotiation phase. For example, a UAV company could theoretically offer its clients a discount on their services in exchange for proprietary rights. (AgTech companies are doing this too: Farmobile offers “profit-sharing” to customers whose data gets resold). According to Ian Glenn, Founder and CEO/CTO of ING Robotic Aviation Inc., “This could save the client money and provide the UAV company with more flexibility.”
Tammer has said in the past: we’re already sharing our data every time we log in to Google. The advertising economy is based on this premise. Drones are collecting valuable data – we know that. And they offer significant benefits over other earth observation techniques. But like AgTech, it will be difficult to leverage the data until a paradigm shift occurs in data sharing practices.
1 Technavio, Global Commercial-Purpose Drones Market: 2015-2019, 5.
2 Technavio, Global Commercial-Purpose Drones Market: 2015-2019, 30.
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By Matt Dancho, Business Science
Data science for business (DS4B) is the future of business analytics yet it is really difficult to figure out where to start. The last thing you want to do is waste time with the wrong tool. Making effective use of your time involves two pieces: (1) selecting the right tool for the job, and (2) efficiently learning how to use the tool to return business value.
REASON 1: R HAS THE BEST OVERALL QUALITIES
There are a number of tools available business analysis/business intelligence (with DS4B being a subset of this area). Each tool has its pros and cons, many of which are important in the business context. We can use these attributes to compare how each tool stacks up against the others! We did a qualitative assessment using several criteria:
Further discussion on the assessment is available on Business Science website.
What we saw was particularly interesting. A trendline developed exposing a tradeoff between learning curve and DS4B capability rating. The most flexible tools are more difficult to learn but tend to have higher business capability. Conversely, the “easy-to-learn” tools are often not the best long-term tools for business or data science capability. Our opinion is go for capability over ease of use.
Of the top tools in capability, R has the best mix of desirable attributes including high data science for business capability, low cost, and it’s growing very fast. The only downside is the learning curve. The rest of the article explains why R is so great for business.
REASON 2: R IS DATA SCIENCE FOR NON-COMPUTER SCIENTISTS
If you are seeking high-performance data science tools, you really have two options: R or Python. When starting out, you should pick one. It’s a mistake to try to learn both. Your choice comes down to what’s right for you. The difference between the R and Python has been described in numerous infographics and debates online, but the most overlooked reason is person-programming language fit. Don’t understand what we mean? Let’s break it down.
Now that we recognize what’s important, let’s learn about the two major players in data science.
Python is a general service programming language developed by software engineers that has solid programming libraries for math, statistics and machine learning. Python has best-in-class tools for pure machine learning and deep learning, but lacks much of the infrastructure for subjects like econometrics and communication tools such as reporting. Because of this, Python is well-suited for computer scientists and software engineers.
R is a statistical programming language developed by scientists that has open source libraries for statistics, machine learning, and data science. R lends itself well to business because of its depth of topic-specific packages and its communication infrastructure. R has packages covering a wide range of topics such as econometrics, finance, and time series. R has best-in-class tools for visualization, reporting, and interactivity, which are as important to business as they are to science. Because of this, R is well-suited for scientists, engineers and business professionals.
WHAT SHOULD YOU DO?
Don’t make the decision tougher than what it is. Think about where you are coming from:
Think about what you are trying to do:
REASON 3: LEARNING R IS EASY WITH THE TIDYVERSE
Learning R used to be a major challenge. Base R was a complex and inconsistent programming language. Structure and formality was not the top priority as in other programming languages. This all changed with the “tidyverse”, a set of packages and tools that have a consistently structured programming interface.
When tools such as dplyr and ggplot2 came to fruition, it made the learning curve much easier by providing a consistent and structured approach to working with data. As Hadley Wickham and many others continued to evolve R, the tidyverse came to be, which includes a series of commonly used packages for data manipulation, visualization, iteration, modeling, and communication. The end result is that R is now much easier to learn.
R continues to evolve in a structured manner, with advanced packages that are built on top of the tidyverse infrastructure. A new focus is being placed on modeling and algorithms, which we are excited to see.
Further, the tidyverse is being extended to cover topical areas such as text (tidytext) and finance (tidyquant). For newcomers, this should give you confidence in selecting this language. R has a bright future.
REASON 4: R HAS BRAINS, MUSCLE, AND HEART
Saying R is powerful is actually an understatement. From the business context, R is like Excel on steroids! But more important than just muscle is the combination of what R offers: brains, muscle, and heart.
R HAS BRAINS
R implements cutting-edge algorithms including:
These tools are used everywhere from AI products to Kaggle Competitions, and you can use them in your business analyses.
R HAS MUSCLE
R has powerful tools for:
R HAS HEART
We already talked about the infrastructure, the tidyverse, that enables the ecosystem of applications to be built using a consistent approach. It’s this infrastructure that brings life into your data analysis. The tidyverse enables:
REASON 5: R IS BUILT FOR BUSINESS
Two major advantages of R versus every other programming language is that it can produce business-ready reports and machine learning-powered web applications. Neither Python or Tableau or any other tool can currently do this as efficiently as R can. The two capabilities we refer to are rmarkdown for report generation and shiny for interactive web applications.
Rmarkdown is a framework for creating reproducible reports that has since been extended to building blogs, presentations, websites, books, journals, and more. It’s the technology that’s behind this blog, and it allows us to include the code with the text so that anyone can follow the analysis and see the output right with the explanation. What’s really cool is that the technology has evolved so much. Here are a few examples of its capability:
Shiny is a framework for creating interactive web applications that are powered by R. Shiny is a major consulting area for us as four of five assignments involve building a web application using shiny.
It’s not only powerful, it enables non-data scientists to gain the benefit of data science via interactive decision making tools. Here’s an example of a Google Trend app built with shiny.
REASON 6: R COMMUNITY SUPPORT
Being a powerful language alone is not enough. To be successful, a language needs community support. We’ll hit on two ways that R excels in this respects: CRAN and the R Community.
CRAN: COMMUNITY-PROVIDED R PACKAGES
CRAN is like the Apple App store, except everything is free, super useful, and built for R. With over 14,000 packages, it has most everything you can possibly want from machine learning to high-performance computing to finance and econometrics! The task views cover specific areas and are one way to explore R’s offerings. CRAN is community-driven, with top open source authors such as Hadley Wickham and Dirk Eddelbuettel leading the way. Package development is a great way to contribute to the community especially for those looking to showcase their coding skills and give back!
You begin with R because of its capability, you stay with R because of its community. The R Community is the coolest part. It’s tight-knit, opinionated, fun, silly, and highly knowledgeable… all of the things you want in a high performing team. Visit Business Science website for a list of Conferences, Social Channels, and Meetups
R has a wide range of benefits making it our obvious choice for Data Science for Business (DS4B). That’s not to say that Python isn’t a good choice as well, but, for the wide-range of needs for business, there’s nothing that compares to R. In this article we saw why R is a great choice. In the next article we’ll show you how to learn R in 12 weeks.
About the Author
Matt Dancho is an R developer and R/Finance 2017 speaker. He is the Founder and CEO of Business Science.
Business Science specializes in “ROI-driven data science”. Their focus is machine learning and data science in business and financial applications. Business Science team builds web applications and automated reports to put machine learning in the hands of decision makers. Visit the Business Science University to learn more about their virtual workshops on how to apply data science and machine learning in real-world business applications. They focus on modeling problems, creating interactive data products, and distributing solutions within an organization.
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