Top 5 Artificial Intelligence & Machine Learning Blogs You Must Follow

Machine learning and artificial intelligence have come a long way over the past few years, but still remain fairly niche topics. If you are new to these technologies or would like to stay updated on new developments, these five blogs should be on your reading list.

The blogs included here are written by leading industry experts who can explain complicated concepts in simple terms that anyone can understand. In order to compile this list, we started with an open-ended Google search to find the top blogs about machine learning and artificial intelligence today.

#1. OpenAI

OpenAI is a non-profit research company founded by Elon Musk, Sam Altman, Peter Thiel, Reid Hoffman, Jessica Livingston, Amazon Web Services, and other high-profile members of the tech community with the goal to build safe artificial intelligence.

If you're interested in how AI works or how it may impact your daily life (healthcare applications), this blog is one that you can't miss. Overall, you can follow AI services evolution by reading its blogs.

#2. Distill

Distill aims to make Machine Learning (ML) and Artificial Intelligence (AI) more accessible to readers. Traditional research can be difficult to consume, so Distill communicates ML research in appealing interactive data visualizations.

It acts as a neutral platform for multiple authors to publish together, and articles are peer-reviewed before publishing - appearing in Google Scholar. Distill is also registered with the Library of Congress and CrossRef.

#3. Machine Learning Mastery

Machine Learning Mastery is written by Peter Harrington, a successful business leader with more than thirty years of experience in the computer industry. He holds degrees in law and mathematics.

After graduating from Oxford University, he worked as a journalist for The Times newspaper in London. When he left his job to start up a company providing language software solutions, Peter became CEO of the now world-leading technology firm ICLP.

#4. The BAIR Blog

The BAIR blog, founded by the Berkeley Artificial Intelligence Research Lab (BAIR), covers AI news from the point of view of researchers. Topics include technical reports on the latest AI developments, insights into how to make AI systems more efficient.

And debates about how best to build safe and usable artificial intelligence systems, and profiles of influential academics in the field.

#5. FastML

FastML tackles interesting topics in machine learning with entertaining, informative posts. Run by economist Zygmunt Zając, it is a go-to platform for those who are looking for an easier way to understand the often confusing subject of ML through understanding pointers and chatbots among other things.

For those who are feeling discouraged from reading some of the more challenging articles about ML because they can't seem to wrap their head around advanced mathematics - take solace in FastML!

#.6. Apple Machine Learning Journal

Apple has been pioneering new methods of machine learning technology in recent years- from implementing speech recognition to predictive text and autocorrect for Siri. Now, they're taking this one step further with their newest iPhone Xs featuring ML capabilities.

It is made possible by a state-of-the-art processor which performs trillions of operations per second. The way Apple Machine Learning Journal captures what it means for the company to integrate these groundbreaking innovations. Overall,

Artificial Intelligence solutions show us just how far we've come when it comes to using AI to change lives (and make them easier).

#7. AI at Google

It is no surprise then, that Google has been consistently at the forefront of development in machine learning. In fact, their technologies use machine learning and artificial intelligence (AI) extensively.

From creating new ways to find information online to redefining what it means to navigate a destination; from helping people find directions via Google Maps to inventing an entirely new way for humans to interact with cars.

Wrapping Up

As machine learning matures, its capability to automate tasks becomes increasingly apparent. To keep up with the latest developments in this field, follow these five blogs. AI machines have been making some pretty impressive strides lately.

If you want to learn about these advancements and leverage them, you must follow AI & Machine learning development journals and blogs.

How Vertical Intelligence Helps Meet Business AI and Data Challenges

Artificial intelligence has the potential to deliver significant business value to companies. However, to reap the benefits, you need to focus on developing artificial intelligence solutions to meet real data challenges rather than focusing on the technology itself. Intelligence is driven by tasks and eventually comes at a later development stage than consciousness. Today, AI has truly revolutionized the business landscape. But nowadays, vertical intelligence is trending in the business world. It is a perfect combo of human expertise and big data analytics implemented with surgical accuracy and timing.

Human expertise is about the capabilities and skills of a person, especially those gained through education and training, that increase revenue generation. Big data analytics solutions leverage advanced analytic techniques against extremely large and diverse data sets. These data sets comprise structured, semi-structured, and unstructured data from various sources and in different sizes ranging from terabytes to zettabytes.

Vertical Intelligence demands human skills as it involves more than just model deployment. VI focuses on helping businesses overcome all challenges in a highly complicated and complex digital world.

How Vertical Intelligence Help Reduce Business AI and Data Problems

Vertical intelligence can help drive business growth by combining industry-specific technology and human expertise to make the best use of your existing stack, staff, and data holdings. VI is a cutting-edge industry intelligence platform that comes with numerous industry reports and granular economic data from more than 3000 nations.

This will assess how to navigate and use the research, data, and customizable resources for various use cases. These include detailed call prep, selling techniques, and risk mitigation tools for financial service providers. The vertical progress of intelligence provides greater neural connectivity. However, these intelligent behaviors seen in microorganisms are independent of neural systems.

Since every organization is different, there should be different vertical solutions for all. Instead of buying numerous software packages and then recruiting computer scientists to convert those generic solutions into workable assets for your organization, a vertical intelligence solution provider offers industry-specific capabilities along with custom algorithm and data management systems.

An organization dealing with actionable data can significantly benefit from the incorporation of a vertical intelligence solution. VI helps in identifying solutions to complicated business challenges in a human-like fashion. This reflects incorporating traits of human intelligence and applying them as algorithms in a machine-friendly manner.

Working hours of the personnel can be reduced dramatically and human brains can be leveraged in highly creative business aspects such as innovation, research, and brainstorming.

Final Thought

The benefits of vertical intelligence are helpful in technology networks, predictive and prescriptive data analytics, big data analytics and process automation, data science application, and propensity modeling.

Additionally, it led to the emergence of effective solutions for vertical expertise, analytics staff augmentation, as well as process review, and optimization. It audits and analyzes business artificial intelligence solutions and their data challenges. VI solutions combine the industry-specific application and expertise to offer the solutions and technology you really need. Moreover, these solutions are highly predictive and drive business automation specific to your use case and requirement.

If you want to overcome your business AI and data challenges and drive business growth, you should consider partnering with an artificial intelligence solutions expert who can help you leverage the benefits of VI implementation.

Know Why Humans Are The Future of Artificial Intelligence

The power of automation and Artificial intelligence services relies on re-imaging the ways we perform tasks and resolve challenges. However, we can only achieve these goals when enterprises prepare themselves to absorb and adopt the trending, new technologies.

Businesses are now realizing the benefits that humans can reap with machines; scaling with speed, data with understanding, decisions with confidence, and outcomes with accountability. The amplified power of AI is here to explore.

Read more: https://orangemantra.mystrikingly.com/blog/know-why-humans-the-future-artificial-intelligence

Know How AI & ML-based Technologies Empower New-Age NBFCs

The financial industry is witnessing a paradigm shift in the business model where customized solutions are offered throughout the customer’s lifecycle. Today, financial institutions strive to offer tailor-made solutions straight from the customer’s acquisition to the collection stage using Artificial intelligence solutions and Machine Learning (ML). This process of using big data, analytics, and automation to help institutions sell the right product to the right customer at the right time results in hyper-personalization, which again is the need of the hour.

Read more: https://topwebdevelopmentcompanies.wordpress.com/2022/08/18/know-how-ai-ml-based-technologies-empower-new-age-nbfcs/

Leading 5 ‘No-Code’ Machine Learning Platforms in 2022

To deploy artificial intelligence and machine learning categories, no-code ML comprises obtaining a no-code development platform with an optical, code-free, and repeatedly drag-and-drop interface. That is why artificial intelligence solutions are in demand in the app development market.

Read more: https://www.newsplana.com/leading-5-no-code-machine-learning-platforms-in-2022/

Top Benefits and Practical Issues in AI and Machine Learning

Machine learning or ML refers to one of the most successful Artificial Intelligence solutions that provide systems with automated learning without any constant programming or coding. Over the years, machine learning has acquired a lot of features due to its capabilities applied across ventures to resolve complex challenges with ease. From digital assistants that play on-demand music to the products, you are suggested on the basis of prior search history.

Machine learning is gaining popularity as companies require software that can grasp data and provide data accuracy. The core objective of machine learning is to perform optimal functions hassle-freely.

Why Choose Machine Learning?

Machine learning is defined as a segment derived from Artificial Intelligence solutions that enhance the quality of applications by implementing the previously assimilated data. It programs systems to adopt and fetch data without the need to apply any codes for every new similar activity the user performs.

The Machine Learning domain is continuously evolving with high demand in the market. All thanks to its ability to deliver real-time results without any human intervention. It also helps analyze and assess large amounts of data with ease by creating data-driven models. Today, Machine Learning is one of the most efficient ways for firms to build strategic business models.

Benefits of Machine Learning

If you are wondering whether or not to invest in Machine Learning for IoT application development, here are the benefits of implementing Machine Learning to your business models:

• Zero human intervention

• Analyze a large amount of data

• Highly efficient than traditional data analytical methods

• Identifies trends and patterns with ease

• Reliable and efficient

• Less workforce required

• Manages a wide array of data

• Accommodates most forms of applications

Common Practical Issues in Machine Learning

Machine Learning is creating a huge impact on data-driven business decisions worldwide. It has also helped enterprises with the correct intel to make informed, data-driven decisions that are faster than traditional methodologies. However, there are many practical issues in Machine Learning that one cannot overlook despite its high efficiency and productivity. Some of these issues include:

Lack of Quality Data

One cannot expect refined data in Machine Learning. While upgrading, algorithms tend to exhaust the developer’s time. As a result, the data quality is either incomplete, unclean, or noisy. One of the reasons for this can be:

• Inaccurate Predictions - which often results in less accuracy in classification and low-quality results.

• Incorrect or incomplete information can lead to faulty programming via Machine Learning. With inadequate information, fetching accurate results is an overwhelming task to accomplish

• The generalizing of input and output of historic data is crucial. However, the most common challenge that occurs is the output becomes difficult to generalize.

Implementation

Enterprises examine the engines regularly before they decide to switch to ML. Using the fresher ML strategies in the existing environment becomes a complicated errand. Keeping up with the legitimate documentation and interpretation becomes crucial to facilitate the maximum usage of ML. However, some issues that may come to implementation include:

• Slow deployment: The models of Machine Learning are time efficient. However, the creating process of these models says otherwise.

• Data security: Saving confidential data on ML servers is a risky process since the model won’t differentiate between sensitive and critical data

• Lack of data is another challenge faced during the implementation of the ML model. With no accurate data, it is impossible to fetch valuable output.

Obsolete Algorithms with Data Growth

ML algorithms require consistent data while getting trained. These ML algorithms are trained over a specific data index and used to forecast future data. However, the challenge occurs when the previous “accurate” model over the data set may not get considered in the present if the arrangement of data changes.

Summary

Lastly, there may be many issues and challenges in Machine Learning. However, it is one of the most evolving industries with advanced technological developments. Many giant-tech companies seek help from Machine Learning Development Company to assist their large-grouped data analytics. From medical diagnosis and developments to predictions and classifications, ML plays a crucial role in every field.

Are you interested in ML projects? We can help you. Let’s connect today.