With its incredible potential to analyze data for driving meaningful insights, enterprises worldwide are leveraging the benefits of this…
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Image GPT: Generative Image one pixel by pixel
So now the GPT model is generating images and pixel values, it was originally used to generate text but now it is doing pixel generation…
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How Machine Learning Will Affect Software Development
How Machine Learning Will Affect Software Development?
High-grade software
Modern software systems release “machine data” (logs, metrics, etc.) that are critical to detecting and understanding the abuse, but the size and complexity of this data far exceeds the human ability to perform the necessary analysis and capture.
Timely action. For this reason, we see a lot of possibilities to create automated systems that analyze (and work on) these machine learning data to improve the security, performance, and reliability of financially complex software services.
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There is also a lot of exciting research around “ML on code”: automated detection of risky pull requests, automatic bug localization, intelligent IDE help, and so on. Given the well-known challenges of building and operating software systems, there is much room for improvement over the entire lifecycle. Overall, I think we are going through a really interesting time to apply ML techniques to software development, security, and operations.
ML has tremendous opportunities in continuous automation of bug fixing, testing, deployment, and code optimization.
There is no possibility of replacing the human factor in software development. ML could not decide which was right or wrong. It will continue to identify more tests that can be automated. By solving and automating small tasks daily to make intelligent decisions, you will be able to deliver more, faster, more quality, and with less human involvement.
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The great promise lies in the pace of development and production — enabling us to do so much more with our time. The simplicity with which we introduce feedback and iterative cycles has allowed us to redirect and focus on outcome-led programming. Capacity beyond what humans can achieve in software development.
There are also many possibilities for making the process of creating and producing software very fast. But for me, the opportunity lies in the possibility of humans and machines working together — moving the role of the programmer, developing new skills and freeing them to focus on what they are good at, and allowing the machines to be mundane.
Other
There are a lot of areas in tech that are going to see huge improvements from ML in the coming years, but I am very excited about Discoverability — the process of finding a product or experience. From grocery shopping to finding flights, searching for information on Google, we all spend many hours a week doing these things.
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Teaching our priorities to the computer can help us do these things faster. We get more free time, and in contrast to sectors like self-driving cars, no one will lose a job. It is a pure universal good, and a machine learning helps.
Next-generation applications that use ML are seamlessly integrated and are in the fabric of the ML app, so ML is working on real-time data, re-training, and testing, and decision-making. Develop an integrated platform that integrates ML into the data platform to power the required data size.’
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Allowing humans to focus on things like creativity and non-linear thinking requires problems. Automate repetitive tasks. AI services are what builds intelligence. Use AI to automate specific tasks around cleaning and preparing data and dashboard creation. How to deal with technology without BI training.
More data scientists. With more open source ML libraries it is more accessible to software developers.
There seems to be a great deal of potential for combining classical robotic algorithms with ML. ML improves the performance of some of the algorithms while maintaining the transparency of the original method.
DevOps can be a big winner. This becomes part of the software development process as people begin to find value in the Ops data. Developers use tools if they are part of the toolchain everyone uses. If ML-based data is part of the feedback loop of the dev pipeline and code processes they are likely to use it. Subtle improvement in quality that touches the intended features. They can see change for the better or worse over time.
ML SaaS-if is even more so. Some common problems come with the best models and people are licensed and tailored to their use case. We stop building ML models from scratch. We bring the best race to this is for application.
I believe that outside of most profitability, there is a vertical-centric approach. It is difficult to build a one-size-fits-all horizontal solution, and successful companies can spend their time in a column and make money from a specific problem.
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How Machine Learning Will Affect Software Development was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Machine Learning Engineer vs Data Scientist (Is Data Science Over?)
What has been happening to the definition of Data Scientist over the past 5 years? Does it still exist or has it morphed into a new version of its old self? Learn more about the recent trends in job descriptions and salaries for data scientists, ML engineers, and others to best understand the best fit for your career trajectory and interests.
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Here is a selection of courses for those interested in diversifying their domain knowledge into the related realms of economics and finance, with the goal of being able to apply your data science skills to these domains.
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What Is an ARIMAX Model?
What is the ARIMAX model?
If you’ve read our series of blog tutorials on models for estimating time series data, you’re already familiar with 3 major approaches – autoregression, moving averages and integration.
What’s the common theme in all these models?
They solely relied on a single variable.
However, a model can also take into account more than just past prices or past residuals.
And these are the so-called “MAX” models, with the ARMAX being the non-integrated version and the ARIMAX – its integrated equivalent.
So, in this tutorial, we’re going to explore what they look like and show you how to implement them into Python step-by-step.
Let’s get started, shall we?
Why Are ARMAX and ARIMAX Called “MAX” Models?
The names ARMAX and ARIMAX come as extensions of the ARMA and ARIMA respectively. The X added to the end stands for “exogenous”. In other words, it suggests adding a separate different outside variable to help measure our endogenous variable.
The ARMAX and ARIMAX Model Equation:
Since the only difference between the ARMAX and the ARIMAX is that one is integrated and the other one isn’t, we can examine one of them and then highlight how the other one would differ.
We explored an integrated model in our last blog article (ARIMA), so let’s see what the equation of the ARIMAX looks like.
ΔPt =c+βX+ϕ1 ΔPt-1 + θ1 ϵt-1+ϵt
Of course, the equation for the ARMAX would be the same, except we would use the actual variable, say P, instead of its delta.
Pt=c+βX+ϕ1 Pt-1+ θ1 ϵt-1 +ϵt
Breaking Down the ARIMAX Equation:
We can think of the ARMAX as a special case of the ARIMAX, where the order of integration is 0.
So, for the rest of the tutorial, we’ll focus on the ARIMAX.
And we’ll begin by breaking down the different parts in it.
For starters, Pt and Pt-1 represent the values in the current period and 1 period ago respectively.
Similarly, ϵt and ϵt-1 are the error terms for the same two periods. And, of course, c is just a baseline constant factor.
The two parameters, ϕ1 and θ1, express what parts of the value Pt-1 and error ϵt-1 last period are relevant in estimating the current one.
Now, the two new additions to the model are “X” and its coefficient β. Just like ϕ, β is a coefficient which will be estimated based on the model selection and the data. But what about X?
What is an exogenous variable?
Well, X is the exogenous variable and it can be any variable we’re interested in.
It can be a time-varying measurement like the inflation rate or the price of a different index. Or a categorical variable separating the different days of the week. It can also be a Boolean accounting for the special festive periods. Finally, it can stand for a combination of several different external factors.
The idea is that it can be any other variable or variables that can affect prices, as long as we have the data available.
Such outside factors are known as exogenous variables in our regression. We use their values to predict and explain the one we’re interested in, which happens to be current prices in our case.
How to Implement ARMAX and ARIMAX Models in Python?
Conveniently enough, the statsmodels package comes in with a method called ARIMA which is fully capable of handling such additional inputs.
We start by specifying the model characteristics and the orders of the model:
After we’ve done that we also need to specify the exogeneous argument called “exog”.
The value we want to pass needs to be an array of some sort since we wish to have values associated with every time-period.
For instance, we can use S&P prices as this exogenous variable, since we already have them in our data.
Now, we’re ready to fit an ARIMAX (1,1,1) model.
Make sure to name your model variable in a way that distinguishes it from similar models. In this case, we choose to do this by adding “X, spx” at the end to indicate that the exogeneous variable is the S&P.
Then, as can be seen from the snippets, we set this equal to the ARIMA method as before, we add the time-series, and the order, as we’re used to. Finally, between the two, we set the “exog” argument equal to “DF SPX”, which indicates the S&P prices.
If we fit this model and print its summary table, we’re going to see that we get an additional row for the S&P prices.
And that’s all there is to it!
We’ve successfully seen how to implement an ARIMAX model in Python.
If you want to learn more about ARIMAX and other time series models in Python, make sure to check out our step-by-step Python tutorials.
If you’re new to Python, and you’re enthusiastic to learn more, this comprehensive article on learning Python programming will guide you all the way from the installation, through Python IDEs, Libraries, and frameworks, to the best Python career paths and job outlook.
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Five Lines of Code
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What Impact Do AI and ML Have on Security Testing?
When it comes to combating an ever-increasing number of cyberattacks and vulnerabilities in what can only be termed as an ‘ever-evolving’ threat landscape, enterprises need to realize the importance of frequent security tests, and the role that they play in fostering cybersecurity within organizations.
On paper, the prospect of routinely testing the deployed software and programs in an organization seems to be a feat almost too easy to accomplish. Quite on the contrary, the reality of the security testing phenomenon could not be any more different. In order to ensure that the software being tested is completely secure, most organizations rely on a lot of context clues, which include all the relevant bits of information and data. A disadvantage of this, however, is that companies are expected to bear the costs of the actual tests, along with all the manual labor that goes into the gathering of such a large amount of data.
Fortunately, as the cybersecurity world welcomes with open arms the multiple benefits that artificial intelligence and machine learning have to offer, a rather unprecedented advantage that comes into light is the enormous (positive) impact that AI could have in the software security testing process as a whole.
Not only can AI and ML automate several menial testing processes and therefore, save a lot of valuable resources, their inclusion in an organization’s security testing can also immensely improve the overall value of the testing process by delivering near-flawless results.
Long gone are the days where organizations could only dream about harnessing the power of artificial intelligence and machine learning since more and more industries are integrating AI. Some ways through which the capabilities of AI and ML can be steered to bring about a more effective and reliable security testing process include the following:
#1- Gathering of Data Made Easy:
As we’ve already discussed above, one of the most critical aspects of ensuring that the results of the security tests are as flawless as possible is through the use of information. Simply put, the larger that your data pool is, the higher are the chances of the security test being conducted successfully.
Since a software security testing process makes use of a large data set, gathering all that information could prove to be extremely labor and time consuming, which is where the advantages of AI can be made use of.
The inclusion of AI can result in the automation of the data collection process, and end up saving the valuable time of security teams as well. Furthermore, for an even more effective approach to the security testing process, an organization could have it’s security teams combine both AI and ML systems, which covers both the software and hardware component, and accounts for every computer and device active on the network.
#2- Harnessing the Power of Machine Learning in Application Scanning:
One of the key aspects of security testing is application scanning, which reveals to the security teams all the smallest and biggest issues present within the application being tested. Having said that, organizations can amalgamate Machine Learning to application scans in order to minimize the amount of manual labor required to identify the vulnerabilities present on a network.
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Having said that, however, the discoveries made by the ML-enabled application scans should always be second-checked by the organization’s test engineers to determine whether the findings are accurate or not. Moreover, the security team also needs to prioritize the discovered vulnerabilities, and remedy them accordingly.
Perhaps the biggest advantage in using machine learning optimized application scanning tools is that it fosters more reliable results by filtering out any chunks of information that are irrelevant. In other, simpler words, machine learning offers increasingly reliable results by focusing on a smaller data set, rather than analyzing the whole data set.
And if that wasn’t enough, the integration of machine learning into application scanning also significantly reduces the time required for security testing, since it enables the automation of the new application scans being conducted.
#3- AI can Foster Security on the Access Stage:
The access stage refers to the multiple devices, or “identities” on the network that are controlled by the security team to discover the vulnerabilities present on the network. It is the same as that of a VPN that protects the user from spying eyes, find out more about it.
After the security testing process is complete, the organization’s test engineers are tasked with the tedious process of ensuring that the network is free from any flaws that might be utilized by hackers, and cybercriminals to gain entry into the system.
The inclusion of AI can greatly benefit the access stage, since the deployment of AI-based algorithms can ensure that the multiple devices and computers on the network are protected, by using several sophisticated and complex passwords.
Additionally, organizations may also rely on machine learning to help them diagnose the vulnerabilities that threaten to expose confidential data, by identifying user patterns, habits, and suggesting steps that could help fix those flawed trends.
#4- AI and ML can be Used to Foster Efficient Security Testing:
Last but certainly not least, one of the most profound ways through which AI and ML can help strengthen the security testing processes in an organization is by providing to organizations more efficient operations, which consequently result in a more efficient result.
Some ways through which AI streamlines the testing process is by the automation of several menial processes, which reduces the need for manual labor investments. Moreover, AI and ML also enable the security teams to deliver a greater level of value, by enabling them to provide more accurate results.
Parting Words:
At the end of the article, we can only hope that we’ve brought into light some of the more unprecedented advantages that AI and ML have to offer, particularly as far as the security testing phenomenon is concerned, which in turn fosters an environment that promotes cybersecurity amidst the ever-evolving threat landscape of today.
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What Impact Do AI and ML Have on Security Testing? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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