What is data science?

How often do you think you’re touched by data science in some form or another? Finding your way to this article likely involved a whole bunch of data science (whooaa). To simplify things a bit, I’ll explain what data science means to me.

“Data Science is the art of applying scientific methods of analysis to any kind of data so that we can unlock important information.”

That’s a mouthful. If we unpack that, all data science really means is to answer questions by using math and science to go through data that’s too much for our brains to process.

Data Science covers:

• Machine learning
• Data visualization
• Predictive analysis
• Voice assistants

And all the buzzwords we hear today, like artificial intelligence, deep learning, etc.

To finish my thought on data science being used to find this article, I’ll ask you to think of the steps you used to get here. For the sake of this explanation, let’s assume that most of you were online looking at pictures of kittens and puppies when you suddenly came across a fancy word related to data science and wanted to know what it was all about. You turned to Google hoping to find the meaning of it all, and you typed “What is *fill in your data science related buzzword*.”

You would have noticed that Google was kind enough to offer suggestions to refine your search terms – that’s predictive text generation. Once the search results came up, you would have noticed a box on the right that summarizes your search results – that’s Google’s knowledge graph. Using insights from SEO (Search Engine Optimization) I’m able to make sure my article reaches you easily, which is a good data science use case in and of itself. All of these are tiny ways that data science is involved in the things we do every day.

To be clear, going forward I’m going to use data science as an umbrella term that covers artificial intelligence, deep learning and anything else you might hear that’s relevant to data and science.

Positives: astrophysics, biology, and sports

Data science made a huge positive impact on the way technology influences our lives. Some of these impacts have been nice and some have been otherwise. *looks at Facebook* But, technology can’t inherently be good or bad, technology is… technology. It’s the way we use it that has good or bad outcomes.

We recently had a breakthrough in astrophysics with the first ever picture of a black hole. This helps physicists confirm more than a century of purely theoretical work around black holes and the theory of relativity.

To capture this image, scientists used a telescope as big as the earth (Event Horizon Telescope or EHT) by combining data from an array of eight ground-based radio telescopes and making sense of it all to construct an image. Analyzing data and then visualizing that data – sounds like some data science right here.

A cool side note on this point: a standard Python library of functions for EHT Imaging was developed by Andrew Chael from Harvard to simulate and manipulate VLBI (Very-long-baseline interferometry) data helping the process of creating the black hole image.

Olivier Elemento at Cornell uses Big Data Analytics to help identify mutations in genomes that result in tumor cells spreading so that they can be killed earlier – this is a huge positive impact data science has on human life. You can read more about his incredible research here.

Python is used by researchers in his lab while testing statistical and machine learning models. Keras, NumPy, Scipy, and Scikit-learn are some top notch Python libraries for this.

If you’re a fan of the English Premier League, you’ll appreciate the example of Leicester City winning the title in the 2015-2016 season.

At the start of the season, bookmakers had the likelihood Leicester City winning the EPL at 10 times less than the odds of finding the Loch Ness monster. For a more detailed attempt at describing the significance of this story, read this.

Everyone wanted to know how Leicester was able to do this, and it turns out that data science played a big part! Thanks to their investment into analytics and technology, the club was able to measure players’ fitness levels and body condition while they were training to help prevent injuries, all while assessing best tactics to use in a game based on the players’ energy levels.

All training sessions had plans backed by real data about the players, and as a result Leicester City suffered the least amount of player injuries of all clubs that season.

Many top teams use data analytics to help with player performance, scouting talent, and understanding how to plan for certain opponents.

Here’s an example of Python being used to help with some football analysis. I certainly wish Chelsea F.C. would use some of these techniques to improve their woeful form and make my life as a fan better. You don’t need analytics to see that Kante is in the wrong position, and Jorginho shouldn’t be in that team and… Okay I’m digressing – back to the topic now!

Now that we’ve covered some of the amazing things data science has uncovered, I’m going to touch on some of the negatives as well – it’s important to critically think about technology and how it impacts us.

The amount that technology impacts our lives will undeniably increase with time, and we shouldn’t limit our understanding without being aware of the positive and negative implications it can have.

Some of the concerns I have around this ecosystem are data privacy (I’m sure we all have many examples that come to mind), biases in predictions and classifications, and the impact of personalization and advertising on society.

Negatives: gender bias and more

This paper published in NIPS talks about how to counter gender biases in word embeddings used frequently in data science.

For those who aren’t familiar with the term, word embeddings are a clever way of representing words so that neural networks and other computer algorithms can process them.

The data used to create Word2Vec (a model for word embeddings created by Google) has resulted in gender biases that show close relations between “men” and words like “computer scientist”, “architect”, “captain”, etc. while showing “women” to be closely related to “homemaker”, “nanny”, “nurse”, etc.

Here’s the Python code used by the researchers who published this paper. Python’s ease of use makes it a good choice for quickly going from idea to implementation.

It isn’t always easy to preempt biases like these from influencing our models. We may not even be aware that such biases exist in the data we collect.

It is imperative that an equal focus is placed on curating, verifying, cleaning, and to some extent de-biasing data.

I will concede that it isn’t always feasible to make all our datasets fair and unbiased. Lucky for us, there is some good research published that can help us understand our neural networks and other algorithms to the extent that we can uncover these latent biases.

When it comes to data science, always remember –

“Garbage in, garbage out.”

The data we train our algorithms with influences the results they produce. The results they produce are often seen by us and can have a lasting influence.

We must be aware of the impact social media and content suggestions have on us. Today, we’re entering a loop where we consume content that reinforces our ideas and puts people in information silos.

Research projects that fight disinformation and help people break out of the cycle of reinforcement are critical to our future. If you were trying to come up with a solution to this fake news problem, what would we need to do?

We would first need to come up with an accurate estimate of what constitutes “fake” news. This means comparing an article with reputable news sources, tracing the origins of a story, and verifying that the article’s publisher is a credible source.

You’d need to build models that tag information that hasn’t been corroborated by other sources. To do this accurately, one would need a ton of not “fake” news to train the model on. Once the model knows how to identify if something is true (to a tolerable degree of confidence), then the model can begin to flag news that’s “fake.”

Crowd sourced truth is also a great way to tackle this problem, letting the wisdom of the crowd determine what the “truth” is.

Blockchain technology fits in well here by allowing data to flow from people all over the world and arrive at consensus on some shared truth.

Python is the fabric that allows all these technologies and concepts to come together and build creative solutions.

Python, a data science toolset

I’ve talked about data science, what it means, how it helps us, and how it may have negative impacts on us.

You’ve seen through a few examples how Python is a versatile tool that can be used across different domains, in industry and academia, and even by people without a degree in Computer Science.

Python is a tool that makes solving difficult problems a little bit easier. Whether you’re a social scientist, a financial analyst, a medical researcher, a teacher or anyone that needs to make sense of data, Python is one thing you need in your tool box.

Since Python is open source, anyone can contribute to the community by adding cool functionalities to the language in the form of Python libraries.

Data visualization libraries like Matplotlib and Seaborn are great for representing data in simple to understand ways. NumPy and Pandas are the best libraries around to manipulate data. Scipy is full on scientific methods for data analysis.

Whether you want to help fight climate change, analyze your favorite sports team or just learn more about data science, artificial intelligence, or your next favorite buzzword – you’ll find the task at hand much easier if you know some basic Python.

Here are some great Python libraries to equip yourself with:

• NumPy
• Pandas
• Scikit-Learn
• Keras
• Matplotlib

I’ll illustrate an example of how easy it is to get started with data science using Python. Here’s a simple example of how you can use Scikit-Learn for some meaningful data analysis.

Python example with Scikit-learn

This code is available at the Kite Blog github repository.

I’ve used one of Scikit-Learn’s datasets called Iris, which is a dataset that consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150×4 numpy.ndarray. The rows are the samples and the columns are: Sepal Length, Sepal Width, Petal Length, and Petal Width.

I’m going to run a simple linear regression to display the correlation between petal width length. The only libraries used here are scikit-learn (for the regression and data set) and matplotlib for the plotting.

from sklearn import datasets, linear_model
import matplotlib.pyplot as plt

iris = datasets.load_iris()

# Data and features are both numpy arrays
data = iris.data
features = iris.feature_names

Now, we’ll plot a linear regression between the length and width of the petals to see how they correlate.

# Create the regression model
regression = linear_model.LinearRegression()

# Reshape the Numpy arrays so that they are columnar
x_data = data[:, 2].reshape(-1, 1)
y_data = data[:, 3].reshape(-1, 1)

# Train the regression model to fit the data from iris (comparing the petal width)
regression.fit(x_data, y_data)


# Display chart
plt.plot(x_data, regression.predict(x_data), color='black', linewidth=3)
plt.scatter(x_data, y_data)
plt.show()

Here’s a tutorial I created to learn NumPy, and here’s a notebook that shows how Keras can be used to easily create a neural network. Just this much will allow you to build some pretty cool models.

Concluding thoughts

Before I end, I’d like to share some of my own ideas of what I think the future of data science looks like.

I’m excited to see how concerns over personal data privacy shapes the evolution of data science. As a society, it’s imperative that we take these concerns seriously and have policies in place that prevent our data accumulating in the hands of commercial actors.

When I go for walks around San Francisco, I’m amazed at the number of cars I see with 500 cameras and sensors on them, all trying to capture as much information as they possibly can so that they can become self driving cars. All of this data is being collected, it’s being stored, and it’s being used. We are a part of that data.

As we come closer to a future where self driving cars become a bigger part of our life, do we want all of that data to be up in the cloud? Do we want data about the things we do inside our car available to Tesla, Cruise or Alphabet (Waymo)?

It’s definitely a good thing that these algorithms are being trained with as much data as possible. Why would we trust a car that hasn’t been trained enough? But that shouldn’t come at the cost of our privacy.

Instead of hoarding people’s personal data in “secure” cloud servers, data analysis will be done at the edge itself. This means that instead of personal data leaving the user’s device, it will remain on the device and the algorithm will run on each device.

Lots of development is happening in the field of Zero Knowledge Analytics which allows data to be analyzed without needing to see what that data is. Federated Learning allows people to contribute to the training of Neural Networks without their data to leaving their device.

The convergence of blockchain technology and data science will lead to some other exciting developments. By networking people and devices across the globe, the blockchain can provide an excellent platform for distributed computation, data sharing, and data verification. Instead of operating on information in silos, it can be shared and opened up to everyone. Golem is one example of this.

Hypernet is a project born out of Stanford to solve a big problem for scientists – how to get enough compute power to run computationally and data intensive simulations.

Instead of waiting for the only computer in the university with the bandwidth to solve the task and going through the process of getting permission to use it, Hypernet allows the user to leverage the blockchain and the large community of people with spare compute resources by pooling them together to provide the platform needed for intensive tasks.

Neural networks for a long time have felt like magic. They do a good job, but we’re not really sure why. They give us the right answer, but we can’t really tell how. We need to understand the algorithms that our future will be built on.

According to DARPA, the “third-wave” of AI will be dependent on artificial intelligence models being able to explain their decisions to us. I agree, we should not be at the mercy of the decisions made by AI.

I’m excited with what the future holds for us. Privacy, truth, fairness, and cooperation will be the pillars that the future of data science forms on

The post Data Science, the Good, the Bad, and the Future appeared first on Artificial Intelligence.

If you’ve ever watched a robot science fiction movie, odds are good you have seen one where the robots try to take over the world. However, if this is your idea of what artificial intelligence is in the real world, maybe it’s time we separate fact from fiction and discuss the reality of AI today.

AI and automation are being used for tedious manual and repetitive tasks, saving companies the cost of hiring employees. AI also helps to improve human decision-making (we need a little help in this area) by using algorithms to solve problems.

Here are a few ways AI will help us improve the workplace in the very near future:

Candidates can find a better fit: Rather than parsing through hundreds of jobs that aren’t the perfect fit, people looking for jobs can use AI to personalize their job searches and focus on the ones they’re qualified for. At IBM, a tool called “Watson Candidate Assist” lets applicants enter their skills to customize the jobs they can apply for.

The training process is improved: Typically, established employees are asked to train new hires, which takes them away from their regular responsibilities. But thanks to technology, new employees can learn from AI coaches who are available throughout the onboarding process, which keeps disruption in the workplace to a minimum.

Ongoing training gets easier: AI improves creativity and learning by personalizing each training experience based on the specific learning patterns of each student. Chatbots will facilitate training, and the company benefits by having employees who are always on top of the latest software, tools, and strategies to do their jobs well.

Productivity will skyrocket: AI will help us save time by scheduling meetings automatically, analyzing large amounts of data, and answering commonly-asked questions. Tools like chatbots can respond to customers’ questions about store hours, products, and shipping policies, removing the need for an actual human to do that task. Replacing simple tasks with technology frees up actual employees to focus on more important work and cuts down on labour costs.

You can decrease workplace stress: The American Psychological Association reports 64% of Americans experience work-related stress. AI shows promise when it comes to well-being technology that can monitor stress and health, like a Fitbit type tool. These cloud-based well-being technologies also can be integrated into an organization’s wellness program to help reduce sick days.

Employees will stay at companies longer: Employees are staying in positions up to 30% longer than they used to. Because of this, some companies are trying to take an active role in helping employees focus on growing their career experience using AI and machine learning to train employees for that next step up the corporate ladder.

Industries facing disruption

A common misconception is that once AI becomes commonplace, jobs will be eliminated and replaced by robots. According to Gartner, by 2020, AI “will actually create more jobs than it eliminates.” However, some industries will change more than others.

Some industries that can expect disruption from AI include:

• Call centers/retail: Chatbots are able to answer repetitive questions in seconds, leaving trained technicians to answer more advanced queries online.

• Manufacturing: Manufacturers are using AI to improve automation and optimization. They are also applying artificial intelligence algorithms to augment decision-making.

• Agriculture: Agriculture is suffering from labour shortages and could benefit from more automation and efficiency. AI could help strengthen the industry.

• Energy: AI will be helpful in several areas of oil and gas, including tracking tankers to predict what has been shipped, arrival times, and more. This can help traders make better decisions.

• Healthcare: AI and automation may help to eliminate diseases. There have already been some great advances that provide more accurate diagnoses, cures, and disease prevention.

Preparing for the AI revolution

By 2020, the Fourth Industrial Revolution will bring us artificial intelligence, machine learning, advanced robotics, biotech, and more. According to McKinsey research, fewer than 5% of jobswill be fully automated, and 60% of jobs may have around 30% of their activities automated. As these advancements change our work and lives, they will cause some jobs to disappear, but also create new jobs that don’t exist today. The workforce of the future is going to have to align its skill sets to keep up.

The key is being open to innovative ways that technology can improve the workplace. Yes, it takes a ramp-up to adapt to these changes, but in the long run, they’re going to revolutionize the way we work. HR managers should look for people who either have experience using technology like AI or who are open to learning how to apply it to their role.

Artificial intelligence is coming, whether your business is ready or not. It’s time to get ready for this phenomenon and find ways to leverage it . . . or you just may end up being left behind.

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The Fourth Industrial Revolution is blurring the lines between the physical, digital and biological spheres, creating breakthroughs in robotics, nanotechnology and artificial intelligence (AI). Underneath the sheer scale and speed of this new age for industry are some human-scale innovations that are changing the nature of difficult, dirty and dangerous work and creating valuable feedback loops that can generate new services, new products and better ways to service customers.

What does this look like on the floor of the average factory or along the power grid or on an oil rig? Sensing and monitoring technologies along the production cycle, together with the data produced by those sensors, are already enhancing safety, predicting potential breakdowns and increasing productivity across industries. But the full potential of AI for manufacturing, utilities, mining and all heavy industry will be measured in more profound ways by changing the value chain and—most significantly—saving lives.

Creating A Safer Work Environment

When a power line goes down in a storm, repair crews face any number of potential hazards working with unstable equipment, dealing with bad weather or encountering unforeseen damage. According to T&D World, utility line work is one of the top 10 most dangerous jobs, with as many as 50 out of 100,000 workers killed on the job every year. Traditionally, equipment inspection might involve line workers climbing poles or standing in cherry pickers, or helicopters flying in low.

A growing network of sensors installed along the power grid is already helping with power-line operation and maintenance, while advanced algorithms are helping to predict potential problems before they happen. Now, drones coupled with intelligent analytics can dramatically streamline routine inspections, increase emergency responsiveness and decrease power outage times, according to eSmart Systems, a company in Norway that specializes in operational technology.

Intelligent drones can also provide radically new insights into grid operations. Drones can fly into areas that would be dangerous for humans, collecting an inventory of assets for electric companies. Ultimately, grid operators can draw on the skills of their frontline repair crews and engineers to help train AI systems to suggest what should be fixed and the cost that it would take to fix it. Time-series data can also be used to create a what-if analysis, allowing for something like a time-travel movie, where users can go back and diagnose what happened or they can go forward to predict what might happen under varying conditions.

It’s not hard to imagine what such monitoring and scenario planning could mean on an offshore oil rig or deep underground at a mining operation—or in any other dangerous occupation where embedded sensors and AI could help predict hazardous conditions before workers are put in harm’s way.

Detecting Defects In Real Time

Since the dawn of the Industrial Age, wasted time and material have been part of the manufacturing process. That expectation has changed—particularly where the product cycle is being compressed to the point that traditional inspection and correction methods are too cumbersome and time-consuming. “For me, creating the ‘Factory of the Future’ means increasing the throughput of products while simultaneously decreasing the need for human touch, because then we’ll remove the chance of waste every time,” explains Matt Behringer, chief information officer, enterprise operations and quality systems, at Jabil, an engineering, manufacturing and supply-chain company.

Collecting more than a million data points from each assembly across its manufacturing process, Jabil is able to anticipate and avert most circuit-board failures early in the production line. As a result, the errors can be corrected prior to adding expensive electronic components or producing defective equipment that ends up in the scrap heap—or worse, on customers’ shelves. The data that Jabil collects from every factory and product worldwide not only improves the inspection processes, but it’s also used to optimize the operation of individual machines in an increasingly intelligent feedback loop.

The technologies that power today’s defect detection systems will be further enhanced by cutting-edge developments such as Field Programmable Gate Arrays (FPGAs). FPGAs can act as local compute accelerators, inline processors, or remote accelerators for distributed computing. FPGA technology can also analyze thousands of images a second—speeds that until now were possible only with a supercomputer—enabling a new kind of precision in any intelligent system that relies on visual information.

Providing Greater Value To Customers After Products Leave The Factory Floor

If it flies at 35,000 feet, drills into layers of hard rock or rolls down the highway, chances are Sandvik Coromant has a machine tooling system that can be used in its production. Since the company was founded 75 years ago, servicing those machines meant sending a technical expert to help on site. Now the company is on a quest to digitize that technical knowledge as well as collect machining data from sensorized tools in order to provide intelligent feedback to the machines and the humans running those machines.

Adding embedded intelligence to these tools captures more data from the machining operation that can be used to automatically make adjustments, notify technicians when maintenance is needed or alert plant managers of a potential failure. By alerting the machine operators when something needs to be changed, they can plan the best time for that action and minimize disruption to ongoing workflows.

Adding Intelligence To The Edge

To achieve true real-time insights, some AI workloads will need to run where they are—at the edge, says research firm ISG. Microsoft CEO Satya Nadella believes that the intelligent edge is part of a foundational technological shift driven in part by the need for distributed, event-driven and server-less technologies that can compute and react where they are located. The ability to add intelligence to edge devices—those devices that don’t rely on a connection to process information or take action—opens new opportunities in difficult environments such as disaster recovery, rail-line inspection and gas pipelines. Innovations, such as the ability to create synthetic environments—like the training tools the military uses to train soldiers and employed by gamers everywhere—can be highly customized and used to train drones and other inspection equipment to recognize anomalies or look for someone in distress.

Smart sensors, AI-embedded machinery, well-trained drones and the breathtaking array of innovation in artificial intelligence today hold great promise to take much of the danger out of difficult, dirty and dangerous work.

The post Difficult, Dirty, Dangerous And Intelligent: How The Human-AI Partnership Is Transforming Industry, Saving Lives And Creating Value appeared first on Artificial Intelligence.

Google and Facebook are teaming up to make each company’s artificial intelligence technologies work better together.

The two companies said Tuesday that an unspecified number of engineers are collaborating to make Facebook’s open source machine learning PyTorch framework work with Google’s custom computer chips for machine learning, dubbed Tensor Processing Units, or TPU. The collaboration marks one of the rare instances of the technology rivals working together on joint tech projects.

“Today, we’re pleased to announce that engineers on Google’s TPU team are actively collaborating with core PyTorch developers to connect PyTorch to Cloud TPUs,” Google Cloud director of product management Rajen Sheth wrote in a blog post. “The long-term goal is to enable everyone to enjoy the simplicity and flexibility of PyTorch while benefiting from the performance, scalability, and cost-efficiency of Cloud TPUs.”

Facebook product manager for artificial intelligence Joseph Spisak said in a separate blog post that “Engineers on Google’s Cloud TPU team are in active collaboration with our PyTorch team to enable support for PyTorch 1.0 models on this custom hardware.”

Google first debuted its TPUs in 2016 during its annual developer conference, and pitched them as a more efficient way for companies and researchers to power their machine-learning software projects. The search giant sells access to its TPUs via its cloud computing business instead of selling the chips individually to customers like Nvidia, whose graphics processing units, or GPUs, are popular with researchers working on deep learning projects.

Artificial intelligence technologies like deep learning have grown in popularity over the years with tech giants like Google and Facebook that use the technologies to create software applications that can automatically do tasks like recognize images in photos.

As more businesses explore machine learning technology, companies like Google, Facebook, and others have created their own AI software frameworks, essentially coding tools, intended to make it easier for developers to create their own machine-learning powered software. These companies have also offered these AI frameworks for free in an open source model in order to popularize them with coders.

For the past few years, Google has been courting developers with its so-called Tensorflow framework as the preferred coding tools for AI projects, and it developed its TPUs to work best with Tensorflow. The fact that Google is willing to update its TPUs to work with Facebook’s PyTorch software shows that the company wants to support more than its own AI framework and potentially gain more cloud computing customers and researchers who may use competing frameworks.

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