The Language of Data Science: Python vs R

The Language of Data Science: Python vs R

Python may be the second choice to R, but its popularity and ease of use positions it to dominate data science.

“When [Netflix’s data science team] started, there was one single kind of data scientist,” says Christine Doig, director of innovation for personalized experiences at Netflix. “Now the role has been integrated into the organization.” This isn’t just a Netflix thing. Across all industries, enterprises are embracing data science to craft personalized, engaging experiences, optimize pricing, and more. As they do so, they’re expanding the use of data science into product management, marketing, and other areas.

This is why the language that organizations use to decipher their data will increasingly be Python, not R. As organizations look to a more diverse group to help with data science, Python’s mass appeal makes for an easy on-ramp.

R or Python?

Historically, if you wanted to do data science, you needed to know R. As detailed on the R project’s site, “R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.” It’s not really a programming language, per se, but includes one. Originally built for statistical and numerical analysis, R has remained true to those roots and remains an excellent tool, particularly for statisticians in their role as data scientists. This strength can also be a weakness, given the spread of data science well beyond the area of statistical analysis.

It’s true, as Sheetal Kalburgi, associate product manager at Anaconda, points out, that “data scientists are more technical and statistical” and often are “responsible for tasks like developing complex statistical algorithms that communicate product performance, predict outcomes, design experiments such as A/B testing, and optimize computational operations, to name a few.” But they also tend to be well versed in programming, which is where your average data scientist is much more likely to have a programming background than a hard-core statistics background.

Even if a company’s business problem centers on statistics, it’s still often going to be the case that Python will prove superior, if only because of familiarity. As Van Lindberg, general counsel for the Python Software Foundation told me, “Python is the second-best language for everything. R may be the best for stats, but Python is the second … and the second-best for [machine learning], web services, shell tools, and (insert use case here). If you want to do more than just stats, then Python’s breadth is an overwhelming win.”

No one really wants the silver medal instead of gold, but in this case, second place means Python will make itself useful for a much broader array of use cases. As Peter Wang, CEO of Anaconda, said in an interview, “Python had a broader scope from the beginning.” Engineering and science DNA is “baked into the Python core.” It’s therefore going to be the right answer much more often than R.

Python swallows data science

That’s not a criticism of R so much as a recognition of the momentum and mass Python has going for it. According to a recent SlashData survey of more than 20,000 developers, Python is a developer darling, coming in second only to JavaScript in terms of popularity. Part of this stems from the huge community around Python that extends Python’s utility into all sorts of domains (deep learning, artificial intelligence, and more) while fine-tuning it in key areas to improve performance. It’s increasingly difficult to find any areas where Python isn’t pushing to be the first-choice option, not merely “second best,” to use Lindberg’s phrasing.

Part of Python’s popularity stems simply from how easy it is to use. Given that enterprises are desperately trying to find data science talent, the easiest path is to mint existing employees. Even those without an engineering background find it easy to embrace Python’s simple syntax and readability and appreciate how useful it is for quick prototyping.

Lately, Python's ease of use has gotten even easier as Anaconda released PyScript, which makes Python more accessible to front-end developers by making it possible to write Python in HTML to build web applications. This is just one more innovation in a long string of innovations in the Python community to expand the breadth and depth of what developers and data scientists can do with Python.

Those innovations, and the Python community that benefits from them, increasingly make the decision to use Python that much easier. For areas where R or another alternative might be first choice, Wang suggests Python’s history as a great glue language means that “maybe someone will build a nice Python wrapper to expose a thin shim to expose some R capabilities” or otherwise make it easy for a data scientist to build with Python while adding complements from other communities, like R.

All this helps explain why Python looks set to help drive the next decade of data science, given how robust it is for experienced data scientists and less-experienced aspirants.

Author: Matt Asay

Source: Infoworld

The Essence of Data Annotation in Machine Learning

The Essence of Data Annotation in Machine Learning

Data annotation in machine learning is a term used to describe the process of labeling data in a way that machines can understand, either through computer vision or natural language processing (NLP). Another way, data labeling enables the machine learning model to perceive its surroundings, make judgments, and take action.

When developing an ML model, data scientists employ many datasets, carefully adapting them to the model’s training requirements. As a result, robots can detect material that has been tagged in a variety of intelligible formats, such as images, texts, and videos.

This is why AI and machine learning businesses are looking for annotated data and annotation service to put into their algorithms, training them to learn and detect recurrent patterns and then using the information to generate exact estimates and forecasts.

Why is Data Annotation Important in Machine Learning?

These things are made possible by data annotation machine learning, whether search engines can increase the quality of their results, improve facial recognition software, or build self-driving cars. Google’s ability to provide results depending on a user’s geographic area or sex, Samsung and Apple’s usage of face unlocking software to increase the security of their devices, Tesla’s introduction of semi-autonomous self-driving vehicles, and so on are all living examples.

Annotated data and annotation service is useful in machine learning for making accurate predictions and estimates in our daily lives. Machines may notice recurrent patterns, make choices, and take action as a result, as previously stated.

In other words, robots are presented with intelligible ways and instructed what to search for – whether it’s in the form of an image, video, text, or audio. There is no limit to how many comparable patterns a trained machine learning algorithm may identify in new datasets.

Latest Trends

Tools that can automatically discover and name things based on comparable hand annotation are known as predictive annotation tools. These technologies may annotate successive frames after the initial few frames are manually tagged in computer vision processes. When selecting a data annotation company, the new significant differentiation is human creativity, which is still necessary for QA and edge cases.

Reporting that is tailored to you. Working with big expert data annotation teams, project progress reporting will become more granular at the individual level and dynamic, thanks to APIs and open source technologies. Throughout the project’s lifespan, this will enable informed decision-making.

Concentrate on quality assurance. When dealing with enormous data sets, teams will be formed that focus only on edge cases and quality control and consist of specialists who have a thorough grasp of the data and its subject matter. They will be able to work without precise instructions and laser focus on detecting and correcting errors in large-scale datasets.

Small- and medium-sized enterprises (SMEs) have a workforce. As more sectors use AI, the demand for subject-specific data annotation teams will grow in healthcare, finance, and government. From the confirmation of guidelines through the moment of data delivery, the experienced data labeler’s focused yet thorough approach provides value to the annotation process.

Conclusion

Data annotation is essential to machine learning and has contributed to some of the cutting-edge technology we have today. Data annotators and annotation company, or the unseen employees in the machine learning industry, are needed today more than ever. The AI and ML industries’ overall success is dependent on the continuing generation of nuanced datasets required to solve some of ML’s most challenging issues.

Annotated data in photos, videos, or texts is the best “fuel” for training ML algorithms, and this is how we get to some of the most autonomous ML models we can potentially and proudly have.

Author: Rayan Potter

Source: Datafloq

How Artificial Intelligence could drive the Electric Vehicles market

How Artificial Intelligence could drive the Electric Vehicles market

Artificial intelligence (AI) is rapidly evolving and becoming ubiquitous across virtually every industry. AI solutions allow organizations to achieve operational efficiencies, gain insights into customer behavior, measure key performance indicators (KPIs), and leverage the power of big data, among other things.

Similarly, the electric vehicles (EV) market has gained traction in recent years. It’s more common to see drivers cruising in EVs, whether a Tesla, Chevy Bolt, or Nissan Leaf. EVs are becoming popular among eco-conscious consumers because they offer more eco-friendly benefits than traditional gas-powered vehicles.

EVs have shown growth throughout the decade and great promise, but adoption rates have lagged in the U.S. compared to other countries.

Is it possible for AI to play a role in helping EV adoption in the U.S. and other countries? Here’s how the EV market could leverage AI to increase sales and create a more sustainable transportation system.

Looking at Current EV Adoption

The U.S. has noted increases in EV adoption rates, but rates are still on the low side compared to other regions of the world. According to data from the World Economic Forum, Norway, Iceland and Sweden lead the world in EV adoption.

One main reason other countries have adopted EVs on a larger scale is that it’s common for their governments to offer incentives to consumers. Various policies have incentivized EV purchases in Norway, but the World Economic Forum suggests this may not fly in other countries.

According to the Argonne National Laboratory, a U.S. Department of Energy (DOE) research center, nearly 2.4 million battery EVs have been sold since 2010. A critical aspect of the EV market is implementing the infrastructure to support charging. Many consumers may be hesitant to purchase or lease EVs because they worry about finding charging stations in their area.

The U.S. currently has almost 113,600 EV charging stations, with most of them located in California. The Biden administration announced a plan to allocate $5 billion in the next five years to build up the EV charging network, which will certainly aid in adoption rates.

How AI Can Speed up EV Adoption

Aside from government funding for infrastructure improvements, other factors will play a role in aiding EV adoption. An article from Forbes cites five major factors driving adoption, including:

  • Emissions regulations
  • Technology
  • Cost
  • Overcoming myths about the environmental impact of EVs
  • A fast-changing EV market with various players (Volkswagen, Tesla, Hyundai, Kia, etc.)

AI can be used for various applications, so it’s worth exploring how it can be leveraged in the EV market to drive adoption.

Improving EV Batteries

One piece of technology necessary for EV development is electric batteries. Developing a suitable battery for an EV requires testing various material combinations, and that’s a time-consuming process. 

EV battery manufacturers can leverage AI solutions to sift through vast amounts of data much quicker than a human researcher. For example, a recent IBM project involved developing a battery capable of faster charging without nickel or cobalt. Researchers had to evaluate a set of 20,000 compounds to determine the battery’s electrolytes. Normally, it would take five years to process this data, but it only took nine days with the help of AI.

Additionally, AI can aid in testing batteries for EVs. Algorithms can be trained to predict how they will perform using only a small amount of data. Speeding up battery research and development will improve EVs, thus speeding up adoption.

Smoothing Out EV Charging Demand

A new project in Canada may help manage EV charging when demand is high. It was recently announced that the Independent Electricity System Operator (IESO) and the Ontario Energy Board (OEB) would support an AI project to improve EV charging management. BluWave-ai and Hydro Ottawa are leading the project, and it’s expected to enhance charging operations when energy is in peak demand.

The pilot project is called EV Everywhere. It uses AI to create an online service for drivers and pools batteries’ storage and charging capabilities. The system will automatically gauge customer interests and impacts, smooth out demand peaks, and allow people to capitalize on lower-cost charging during off-peak times.

Enhancing HMIs for Safety

Another factor driving the adoption of EVs is to ensure safety for drivers and passengers. One essential feature in an EV and most modern vehicles is the human-machine interface (HMI), which is needed for controlling and providing signals to various types of automated equipment, including the LED screens found in many EVs.

HMI systems that leverage AI solutions allow drivers to access a voice-enabled smart assistant, additional controls, better EV monitoring, and infotainment. AI-powered HMI systems will become more widely used, helping drive adoption.

AI has many use cases, especially in the EV market. It’ll be interesting to see how manufacturers and other companies leverage AI to encourage adoption.

Expect More AI Use Cases to Drive EV Adoption

It is becoming more popular for drivers to consider purchasing EVs, but adoption rates need to increase to create a more sustainable transportation system. AI will play a significant role in driving sales if more companies find innovative ways to use these solutions. It’s only a matter of time until EVs become the dominant mode of transportation, but leveraging AI will be critical in reaching that point.

Author: April Miller

Source: Open Data Science

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