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Best Practices in Machine Learning

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Best Practices in Machine Learning

Machine learning (ML) has given rise to several practical applications that fulfill real business interests such as saving time and money.

It has the potential to dramatically impact the future of your organization. Through applications such as virtual assistant solutions, machine learning automates tasks that would otherwise need to be performed by a live agent. Machine learning has made dramatic improvements in the past few years, but we are still very far from reaching human performance levels. Many a times, a machine needs the assistance of a human to complete its task. This is why, it is necessary for organizations to learn best practices in machine learning.

For the correct implementation of a machine learning algorithm, organizations are required to study machine learning use cases and execute best practices. Some such best practices include:

‘IMPORTANCE WEIGHT’ OF YOUR SAMPLED DATA

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When your organization has too much data, there is a temptation to take some files and drop rest of them. Dropping data while training your machine learning algorithms can cause several issues. Importance weighting means that if you decide that you are going to sample example X with a 30% probability, then give it a weight of 10/3. Thus, by importance weighting, all of the calibration properties are discussed and addressed.

Reuse Code

You must reuse code between your training pipeline and your serving pipeline whenever it is possible. Batch processing methods are different than online processing methods. In online processing, you have to handle each request as it arrives, whereas, in batch processing you have to combine tasks.

At serving time, you are doing online processing, while training is a batch processing task. For using the code, you can create an object that is particular to your system. You should be able to store the result of any queries or joins in a very human readable way.

Then, once you have gathered all the information, during serving or training, you should be able to run a common method for bridging between the human readable object that is specific to your system and whatever format the machine learning system expects.

Avoid Unaligned Objectives

While measuring the performance of your machine learning system, your team will start to look at issues that are outside the scope of the objectives of your current system. If your product goals are not covered by the existing algorithmic objective, then you must either change your objective or your product goals. For instance, you may optimize clicks or downloads, but make launch decisions based in part on human raters.

Keep Ensembles Simple 

Unified models are those models that take in raw features and directly rank content. These models are the easiest models to debug and understand. However, an ensemble of models works better. To keep things simple, each model must either be an ensemble, only taking the input of other models, or it can be a base model taking many features, but not both

If your organization is having models on top of other models that are trained separately, then combining such models can result in bad behavior. You must use a simple model for ensemble that takes only the output of your “base” models as inputs. You can enforce properties on these ensemble models. For example, an increase in the score produced by a base model should not decrease the score of the ensemble.

Thus, implementing these best practices can ensure successful implementation of machine learning algorithms.


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TECHNOLOGY

What’s Wrong with the Algorithms?

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What's Wrong with the Algorithms?

Social media algorithms have become a source of concern due to the spread of misinformation, echo chambers, and political polarization.

The main purpose of social media algorithms is to personalize and optimize user experience on platforms such as Facebook, Twitter, and YouTube.

Most social media algorithms sort, filter, and prioritize content based on a user’s individual preferences and behaviors. Social media algorithms have come under scrutiny in recent years for contributing to the spread of misinformation, echo chambers, and political polarization.

Facebook’s news feed algorithm has been criticized for spreading misinformation, creating echo chambers, and reinforcing political polarization. In 2016, the algorithm was found to have played a role in the spread of false information related to the U.S. Presidential election, including the promotion of fake news stories and propaganda. Facebook has since made changes to its algorithm to reduce the spread of misinformation, but concerns about bias and polarization persist.

Twitter’s trending topics algorithm has also been criticized for perpetuating bias and spreading misinformation. In 2016, it was revealed that the algorithm was prioritizing trending topics based on popularity, rather than accuracy or relevance. This led to the promotion of false and misleading information, including conspiracy theories and propaganda. Twitter has since made changes to its algorithm to reduce the spread of misinformation and improve the quality of public discourse.

YouTube’s recommendation algorithm has been criticized for spreading conspiracy theories and promoting extremist content. In 2019, it was revealed that the algorithm was recommending conspiracy theory videos related to the moon landing, 9/11, and other historical events. Additionally, the algorithm was found to be promoting extremist content, including white nationalist propaganda and hate speech. YouTube has since made changes to its algorithm to reduce the spread of misinformation and extremist content, but concerns about bias and polarization persist.

In this article, we’ll examine the problem with social media algorithms including the impact they’re having on society as well as some possible solutions.

1. Spread of Misinformation

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Source: Scientific American

One of the biggest problems with social media algorithms is their tendency to spread misinformation. This can occur when algorithms prioritize sensational or controversial content, regardless of its accuracy, in order to keep users engaged and on the platform longer. This can lead to the spread of false or misleading information, which can have serious consequences for public health, national security, and democracy.

2. Echo Chambers and Political Polarization

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Source: PEW Research Center

Another issue with social media algorithms is that they can create echo chambers and reinforce political polarization. This happens when algorithms only show users content that aligns with their existing beliefs and values, and filter out information that challenges those beliefs. As a result, users can become trapped in a self-reinforcing bubble of misinformation and propaganda, leading to a further division of society and a decline in the quality of public discourse.

3. Bias in Algorithm Design and Data Collection

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Source: Springer Link

There are also concerns about bias in the design and implementation of social media algorithms. The data used to train these algorithms is often collected from users in a biased manner, which can perpetuate existing inequalities and reinforce existing power structures. Additionally, the designers and developers of these algorithms may hold their own biases, which can be reflected in the algorithms they create. This can result in discriminatory outcomes and perpetuate social injustices.

4. Democracy in Retreat

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Source: Freedom House

Social media algorithms are vulnerable to manipulation and can spread false or misleading information, which can be used to manipulate public opinion and undermine democratic institutions. The dominance of a few large social media companies has led to a concentration of power in the hands of a small number of organizations, which can undermine the diversity and competitiveness of the marketplace of ideas, a key principle of democratic societies.

How to Improve Social Media Algorithms?

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Source: Tech Xplore

Governments and regulatory bodies have a role to play in holding technology companies accountable for the algorithms they create and their impact on society. This could involve enforcing laws and regulations to prevent the spread of misinformation and extremist content, and holding companies responsible for their algorithms’ biases.

There are several possible solutions that can be implemented to improve social media algorithms and reduce their impact on democracy. Some of these solutions include:

  • Increased transparency and accountability: Social media companies should be more transparent about their algorithms and data practices, and they should be held accountable for the impact of their algorithms on society. This can include regular audits and public reporting on algorithmic biases and their impact on society.

  • Regulation and standards: Governments can play a role in ensuring that social media algorithms are designed and operated in a way that is consistent with democratic values and principles. This can include setting standards for algorithmic transparency, accountability, and fairness, and enforcing penalties for violations of these standards.

  • Diversification of ownership: Encouraging a more diverse and competitive landscape of social media companies can reduce the concentration of power in the hands of a few dominant players and promote innovation and diversity in the marketplace of ideas.

  • User education and awareness: Social media users can be educated and empowered to make informed decisions about their usage of social media, including recognizing and avoiding disinformation and biased content.

  • Encouragement of responsible content creation: Social media companies can work to encourage the creation of high-quality and responsible content by prioritizing accurate information and rewarding creators who produce this content.

  • Collaboration between industry, government, and civil society: Addressing the challenges posed by social media algorithms will require collaboration between social media companies, governments, and civil society organizations. This collaboration can involve the sharing of data and best practices, the development of common standards and regulations, and the implementation of public education and awareness programs.

Conclusion

Social media companies have the power to censor and suppress speech, which can undermine the right to free expression and the democratic principle of an open and inclusive public discourse. It is crucial for technology companies and policymakers to address these issues and work to reduce the potential for harm from these algorithms. Social media platforms need to actively encourage and facilitate community participation in the development and improvement of their algorithms. This would involve setting up forums for discussion and collaboration, providing documentation and support for developers, and engaging with the community to address their concerns and ideas. In order to ensure that the algorithms are fair and unbiased, tech companies need to be transparent about the data they collect and use to train their algorithms. This would involve releasing the data sets used to train the algorithms, along with information about how the data was collected, what it represents, and any limitations or biases it may contain.

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TECHNOLOGY

Daasity builds ELT+ for Commerce on the Snowflake Data Cloud

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Cloud Computing News

Modular data platform Daasity has launched ELT+ for Commerce, Powered by Snowflake.

It is thought ELT+ for Commerce will benefit customers by enabling consumer brands selling via eCommerce, Amazon, retail, and/or wholesale to implement a full or partial data and analytics stack. 

Dan LeBlanc, Daasity co-founder and CEO, said: “Brands using Daasity and Snowflake can rapidly implement a customisable data stack that benefits from Snowflake’s dynamic workload scaling and Secure Data Sharing features.

“Additionally, customers can leverage Daasity features such as the Test Warehouse, which enables merchants to create a duplicate warehouse in one click and test code in a non-production environment. Our goal is to make brands, particularly those at the enterprise level, truly data-driven organisations.”

Building its solution on Snowflake has allowed Daasity to leverage Snowflake’s single, integrated platform to help joint customers extract, load, transform, analyse, and operationalise their data. With Daasity, brands only need one platform that includes Snowflake to manage their entire data environment.

Scott Schilling, senior director of global partner development at Snowflake, said: “Daasity’s ELT+ for Commerce, Powered by Snowflake, will offer our joint customers a way to build a single source of truth around their data, which is transformative for businesses pursuing innovation.

“As Snowflake continues to make strides in mobilising the world’s data, partners like Daasity give our customers flexibility around how they build data solutions and leverage data across the organisation.” 

Daasity enables omnichannel consumer brands to be data-driven. Built by analysts and engineers, the Daasity platform supports the varied data architecture, analytics, and reporting needs of consumer brands selling via eCommerce, Amazon, retail, and wholesale. Using Daasity, teams across the organisation get a centralised and normalised view of all their data, regardless of the tools in their tech stack and how their future data needs may change. 

ELT stands for Extract, Load, Transform, meaning customers can extract data from various sources, load the data into Snowflake, and transform the data into actions that marketers can pursue. For more information about Daasity, our 60+ integrations, and how the platform drives more profitable growth for 1600+ brands, visit us at Daasity.com.

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4 Activities that Automakers Can Digitize Now

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4 Activities that Automakers Can Digitize Now

Digital automaking is supported by technology-driven trends, consumer needs and new developments in artificial intelligence.

Manufacturing, procurement of raw materials, marketing and sales are factors involved in this change.

Digital automaking is a process that combines simulation, three-dimensional visualizations, analytics and several tool partnerships to make automotive manufacturing easier. Since the automotive industry has been undergoing a digital transformation primarily driven by intelligent mobility, it has encouraged the market to adopt new technology and software for modern vehicles. There has also been a growing need to increase industrial processes’ sustainability, environmental friendliness and adaptability. All of this has made automotive digitalization extremely important.

Automotive digitization helps to keep precise control over business operations, which is made possible using modern technologies like ML (machine learning) and AI (artificial intelligence) to improve short- and long-term performance. 

Automotive digitization has also increased the capacity to monitor each component of the supply chain while lowering costs and risks. Digital automaking can offer automotive solutions in terms of better design, time efficiency, and many other industry solutions.

4 Activities that Can Be Digitized by Automakers Now

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1. Manufacturing

Customers desire tailored goods, but they don’t want to pay more than they would for items that are mass-produced. As a result, manufacturing must be more adaptable than ever, leading to mass customization. Thus, the design, fabrication, use and maintenance of products are changing as a result of the digitalization of manufacturing. It is also changing the operations, procedures and energy footprint of supply chains and more. Digital manufacturing enables firms to provide additional options that are tailored to individual customers. Businesses can better understand supply-chain challenges, including inventory levels, delivery status and demand cycles, thanks to digital manufacturing. 

The factories of the future will move from automation to autonomy, strengthening real-time communication between equipment, physical systems, and people. These factories are referred to as smart factories. The most notable advantages of a smart factory are its shop floor connectivity, advanced robotics, flexible automation, augmented and virtual reality systems, and efficient energy management. The general manufacturing sector’s global standards are established by the automotive industry.

Over the past two decades, the automotive sector has expanded tremendously. However, the main elements that will affect whether digitalization is successfully implemented are the significance of realizing a return on investment (RoI) and the willingness of employees at both the top-most and lowest levels of an organization.

2. Supply Chain

By removing the functional barriers that divide different areas, the digitization of the supply chain is a cross-functional process that spans the entire lifecycle of a vehicle or product and involves all company divisions. It allows for an ecosystem that connects all stakeholders, from raw material and component suppliers to logistics companies, dealers and customers.

Utilizing digital technology throughout the entire supply chain allows for real-time monitoring of all supply-chain stages, be it either procurement of raw materials or finished products ready to be delivered or purchased. The evaluation and management of each event’s impacts on the supply chain can help the automation of procedures and the avoidance of potential interruption.

3. Design

Design plays a significant role in the automotive industry. By digitizing design activity, design professionals can test multiple hypotheses before proceeding with the design phase. Digitalization in the designing of products has been enabled by a digital model known as Digital Twins, which represents tangible assets in 3D. Digital twins mirror the complete car or one of its components’ appearance and behavior. With great assistance from sophisticated software, businesses can collect information about configuration, sensors, inspection data, and other details to improve the product’s design.

Automobile manufacturers are among the many industrial firms that recognize digital twins’ possibilities and the potential it has to bring in the best in the business of automobiles. The design and production processes are simplified by 3D representations, improving vehicle performance and cutting costs for the manufacturers. The twin technology is quickly rising to the top of the list of software solutions used in contemporary auto manufacturing, with applications ranging from car design to predictive maintenance to boosting sales using digitally generated models.

4. Marketing

Any marketing strategy aims to tailor the right message to the right set of audiences at the right time. A marketing campaign that appeals to a 45-year-old countryside man might not affect a 23-year-old lady residing in an urban area. Therefore, the impact of marketing combined with the effectiveness of Artificial Intelligence (AI) can be the biggest boon to any business. The automotive industry can enormously benefit in how they market their brand/product by adding the power of artificial intelligence to their current data. It can lead to a strong possibility of purchasing your products early in the sales process, possibly before customers even begin looking for their new car, which is indicated by specific online activities. 

As a result of recent advancements in third-party cookies and mobile advertising identifiers, AI can now assist brands in finding new prospects much more quickly by utilizing data to identify customers with similar characteristics and behaviors. This strategy can potentially increase your prospective customer base and give you an advantage over your competitors. You can identify high-priority targets by identifying the demographic categories that overlap. These solutions don’t require cookies and are more likely to comply with escalating privacy requirements because they rely on behaviors rather than personal data.

The automotive sector has modified its strategy and is now embracing digitization. Digital transformation in the automotive industry still has a lot of gaps to be addressed, but the trend toward digitization is a sign that the stakeholders in the automotive sector will be properly supplied with digital solutions in the coming days. With intelligent technology, and operations across the entire company and all departments, including manufacturing, supply chain, marketing, and sales, digital automaking will help the automotive industry to flourish in this digital era. An increasingly digital supply chain will also dismantle established barriers and greatly enhance communication. Undoubtedly, businesses must adopt a more significant digital transformation to be ready in this competitive automotive industry.

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