Miracle Olughu

About Miracle Olughu

Posts by Miracle Olughu:

Data Science Trends of 2023 - Top 14 Data Science Trends Of 2023 You Should Know

Data Science Trends of 2023 – Top 14 Data Science Trends Of 2023 You Should Know

Data Science Trends of 2023 – Top 14 Data Science Trends Of 2023 You Should Know

Change is the only constant in life, which also holds true for businesses. With time, companies have embraced modern technology to enhance their productivity and maximize their ROI.

The latest buzzwords in the business world are data analytics, big data, artificial intelligence, and data science. Companies are eager to adopt data-driven models to streamline operations and make informed decisions based on data insights.

The COVID-19 pandemic has disrupted industries worldwide, forcing small and large enterprises to adapt quickly to the changing circumstances. This has increased investments in data analytics and science, with data taking centre stage for nearly every organization.

As companies increasingly rely on data analytics to overcome challenges and improve their operations, we are witnessing the emergence of new trends in the industry. Gartner’s AI trends for 2023 are an example of such developments, categorized into three main areas: accelerating change, operationalizing business value, and data and insights distribution.

This blog will explore the most significant data science trends expected in 2023 and how big data and data analytics are becoming crucial for every enterprise, regardless of industry.

Top Data Science Trends of 2023

Having discovered some of the trends that will be mostly seen in 2023, we will discuss them as follows:

1. Big Data On The Cloud 

The issue with data is not its generation but rather the challenges associated with collecting, organizing, and analyzing it effectively. Questions arise regarding how to gather and store data, as well as how to share the resulting insights with others. Fortunately, data science models and artificial intelligence offer solutions to these problems.

However, concerns regarding data storage persist. To address this, many businesses have migrated their big data to cloud platforms, with roughly 45% of enterprises doing so.

The trend towards utilizing cloud services for data storage, processing, and distribution is expected to continue, with public and private cloud services becoming increasingly popular for big data and data analytics by 2023.

2. Emphasis On Actionable Data 

The value of raw, unstructured, and complex data is limited without knowing how to utilize it effectively. The focus is on actionable data, which combines big data and business processes to enable informed decision-making.

Simply investing in expensive data software is insufficient if the data is not analyzed to extract actionable insights. These insights provide a comprehensive understanding of the business’s current status, market trends, challenges, and opportunities.

Actionable data empowers individuals to make informed decisions and act in the business’s best interests. By utilizing insights from actionable data, businesses can optimize their workflows, distribute projects among teams, and increase overall efficiency.

3. Data As A Service- Data Exchange In Marketplaces 

Accessing data through a Data as a Service (DaaS) service is now possible. This means that companies can obtain data from external sources to assist with their business processes, such as Covid-19 data that can be embedded on websites.

However, this also creates potential data privacy issues and companies are implementing procedures to minimize the risk of data breaches or lawsuits.

To address these concerns, data can be transferred from the vendor’s platform to the buyer’s platform with minimal disruption and no data breaches. This trend of data exchange in marketplaces for analytics and insights is expected to become more prominent in 2023. This process is commonly referred to as DaaS.

4. Use Of Augmented Analytics 

Augmented analytics is a field of data analysis that harnesses the power of artificial intelligence, machine learning, and natural language processing to automate the analysis of vast amounts of data.

This technology automates tasks that were traditionally handled by a data scientist, providing real-time insights. This process is much faster than traditional methods and provides more accurate results, allowing for better decision-making.

Augmented analytics assists with data preparation, processing, analytics, and visualization, enabling experts to explore data and generate detailed reports and predictions. This technology can also combine data from both internal and external sources, making it a versatile tool for data analysis.

5. Cloud Automation And Hybrid Cloud Services

Cloud computing services for public and private clouds are automated using artificial intelligence (AI) and machine learning (ML). The application of AI to IT operations is referred to as AIOps. This technology is transforming how businesses view big data and cloud services by providing improved data security, scalability, centralized databases and governance systems, and data ownership at a lower cost.

One of the most significant data trends anticipated for 2023 is the rise in adopting hybrid cloud services. A hybrid cloud combines a public cloud and a private cloud platform.

Public clouds are cost-effective but may not offer high levels of data security. On the other hand, private clouds are more secure but are costly and may not be suitable for small and medium-sized enterprises (SMEs). A hybrid cloud provides a viable solution that balances cost and security to deliver greater agility. This approach optimizes enterprise resources and performance.

6. Focus On Edge Intelligence 

According to predictions made by Gartner and Forrester, edge computing is expected to become a mainstream process by 2023. Edge computing, also known as edge intelligence, involves performing data aggregation and analysis in close proximity to the network.

Businesses across various industries are keen on leveraging the Internet of things (IoT) and data transformation services to incorporate edge computing into their systems. This increases scalability, flexibility, and reliability, improving enterprise performance.

The reduction in latency and increase in processing speed are other advantages of adopting edge computing. When combined with cloud computing services, edge intelligence allows employees to work remotely while simultaneously boosting productivity and enhancing its quality and speed.

7. Hyperautomation 

A major trend in the field of data science in 2023 is hyper-automation, which originated in 2020. According to Brian Burke, the Research Vice President of Gartner, hyper-automation is an inevitable and irreversible process, and any task that can be automated should be automated to enhance efficiency.

Hyper-automation combines automation with artificial intelligence, machine learning, and intelligent business processes to achieve a higher level of digital transformation in businesses.

The fundamental principles of hyper-automation are advanced analytics, business process management, and robotic process automation. In the upcoming years, the trend is expected to continue expanding with a greater emphasis on robotic process automation (RPA).

8. Use Of Big Data In The Internet Of Things (IoT)

The Internet of Things (IoT) is a network of physical objects that are equipped with software, sensors, and advanced technology to enable connection and data exchange between devices over the Internet.

Integrating IoT with data analytics and machine learning can enhance the system’s flexibility, and the machine learning algorithm’s response accuracy can be improved.

Although many large-scale businesses already employ IoT, small and medium-sized enterprises (SMEs) are gradually adopting the trend to manage data better.

This shift is expected to cause significant disruption to conventional business systems, leading to substantial changes in the development and utilization of business processes.

9. Automation Of Data Cleaning 

In 2023, simply having data will not be enough for advanced analytics. As previously mentioned, data that is not properly formatted or contains errors, redundancies, or duplicates are useless for analytics purposes.

This can result in a slower data retrieval process, wasting businesses’ time and money. In fact, the losses incurred by enterprises due to these issues can be significant, amounting to millions of dollars in some cases.

Consequently, many organizations and researchers are seeking ways to automate the process of data cleaning or scrubbing to accelerate data analytics and obtain accurate insights from big data.

Artificial intelligence and machine learning are expected to play a major role in automating the data-cleaning process.

10. Increase In Use Of Natural Language Processing 

Initially, a branch of artificial intelligence, Natural Language Processing (NLP), has evolved into an essential part of business operations that involves analyzing data to identify patterns and trends.

Predictions indicate that by 2023, NLP will enable immediate retrieval of data from repositories, providing access to high-quality information that can lead to valuable insights.

Moreover, NLP offers sentiment analysis capabilities, which allow businesses to understand their customers’ opinions and feelings towards their products and services and those of their competitors.

This information can help companies to tailor their offerings to meet customer expectations and improve overall satisfaction.

11. Quantum Computing For Faster Analysis 

Quantum computing has become a popular research topic in the field of data science. Google is currently working on this technology, which doesn’t rely on binary digits 0 and 1 for decision-making. Instead, it uses quantum bits from Sycamore’s processor, which can solve complex problems in just 200 seconds.

Despite its potential, quantum computing is still in its early stages and requires significant refinement before various industries can widely adopt it. However, it has already impacted and is expected to become an essential component of business processes.

The goal of utilizing quantum computing is to merge data sets for faster analysis and a better understanding of the relationship between multiple models.

12. Democratizing AI And Data Science 

We have observed the rise in popularity of DaaS, and now a similar trend is emerging for machine learning models. Cloud services have become more in demand, which has made it easier to offer AI and ML models as part of cloud computing services and tools.

If you require data visualization, NLP, and deep learning, you can contact a data science company in India to utilize MLaaS (Machine Learning as a Service). MLaaS would be an ideal solution for predictive analytics.

Investing in DaaS and MLaaS means you would not need to establish a dedicated data science team in your organization, as offshore companies provide these services.

13. Automation Of Machine Learning (AutoML)

AutoML, which stands for automated machine learning, can automate several data science procedures, including data cleaning, model training, result prediction and interpretation, and other similar tasks.

Normally, data science teams are responsible for performing these duties. We have already discussed the possibility of automating the data-cleaning process to speed up analytics.

As more companies begin to incorporate AutoML into their operations, other manual processes will likely follow suit. However, this technology is still in its early stages of development.

14. Computer Vision For High Dimensional Data Analytics 

According to Forrester’s predictions, over a third of businesses will utilize artificial intelligence to minimize disruptions in the workplace. Due to the COVID-19 pandemic, organizations have been compelled to change their operational procedures significantly.

Remote work has become necessary for most businesses, and many consider automation a preferable alternative to relying on human workers. One of the latest data science trends for 2023 is utilizing computer vision for high-dimensional data analytics.

This technology helps businesses identify discrepancies, conduct quality checks, ensure safety protocols, accelerate processes, and perform similar functions. Computer vision is particularly prevalent in manufacturing, automating production monitoring and quality assurance.

Mobile App Development – 4 Ways Data Science Can Transform Mobile App Development

Mobile App Development – 4 Ways Data Science Can Transform Mobile App Development

In today’s digital age, data has become a powerful asset that is worldwide. With the widespread use of mobile applications in various industries and businesses, enormous amounts of data are being generated to meet certain trends and twists in technology.
To make the most of this data, businesses need to find smart and efficient ways to derive valuable insights that can drive their growth and make them stand out.
As a result, data science is now being used in mobile app development, thereby, revolutionizing the industry. In this article, we will explore how data science can transform mobile app development for developers and businesses.
But before we dive into that, let us first gain some understanding.

What Is Data Science?

Data science is a multidisciplinary field that utilizes principles from computer engineering, artificial intelligence, mathematics, and statistics to analyze large amounts of data and extract meaningful insights.
By combining programming skills with expertise in mathematics and statistics, data scientists can extract goal-oriented data analytics.
This approach enables businesses to gain a better understanding of their industry domain or business niche, leading to improved performance and business intelligence.. 

How Does Data Science Work In Mobile App Development?

Data science is mainly concerned with computer programming and the development of AI and ML models that can automatically analyze large volumes of data.
The role of a data scientist in mobile app development involves building data processing pipelines, creating machine learning models, designing architecture, and preparing application analytics. These ML models are then passed on to the app developers for integration into mobile apps.
The focus of data science in mobile app development lies in data ingestion, AI & ML model development, and deployment into mobile apps. Additionally, data science helps app developers in modifying and redeploying the ML models to accommodate newly labeled data or changes in model inputs.
However, it is the mobile app developer’s responsibility to create and maintain the mobile application. Data science works as a component to integrate an automated data analysis model, and therefore, data scientists and app developers work together to develop mobile apps.
The use of data science in app development has significant potential because machine learning and artificial intelligence can transform mobile apps.

How Data Science Can Transform Mobile App Development?

Data science has a vital role in mobile app development. Mobile apps typically gather significant amounts of data and analytics, especially for e-commerce, business, and enterprise-scale apps.
To manage such a vast amount of data, mobile apps are integrated with APIs or cloud services. However, these technologies are designed only for gathering information.
Thus, there is a pressing need for a smart technology solution that can efficiently handle large data sizes and help derive business-driven insights. Integrating data science in mobile app development provides additional benefits, including:

1. Understanding User Behaviour

The success of any business relies on its ability to understand its target audience accurately. In today’s mobile-centric world, people use mobile apps and smartphones extensively to access online services, and businesses collect customer data to determine their preferences and interests.
Data science enables mobile apps to intelligently gather information and generate valuable insights for business intelligence.
This, in turn, assists app owners and stakeholders in analyzing user behavior accurately and adjusting their business strategy accordingly. As a result, businesses can accomplish their objectives more efficiently and avoid potential setbacks.

2. Decode Hidden Issues 

Using data science in mobile app development has several benefits, one of which is the ability to identify potential problems that may have been overlooked during development.

Typically, app errors or flaws are only noticed after deployment, leading to increased costs and wasted effort for the development team and app owners.

With data science, however, hidden or underlying issues in mobile apps can be uncovered using programmed algorithms that detect patterns in unstructured and unrelated data.

This is particularly useful in android app development, where data science can be used to identify bugs and technical glitches before final deployment, resulting in a flawless development process..

3. Improve the Development Process

Data science is not limited to analytics alone; it also involves programming and creating automated capabilities that can improve the app development process.

By utilizing data science, developers can incorporate predictive analytics and machine learning models to create apps. This can help in improving different operations, ranging from data collection to obtaining valuable insights.

Therefore, data science enables app development companies and developers to accelerate their deployment and processes, reducing time, effort, and costs. 

4. Offer Personalized User Experience

The success of an app is highly dependent on user experience, as it shapes the brand image. To ensure optimal user experience, businesses are constantly striving to enhance their apps with innovative features and attractive designs.

Despite these efforts, many apps still struggle to engage and retain users. However, by leveraging data science in mobile app development, businesses can provide more personalized and superior user experiences. This, in turn, can improve brand perception and customer loyalty.

Conclusion

Data science is a technology that is both intelligent and revolutionary. It enhances mobile applications by equipping them with automated data analytics capabilities, enabling them to extract valuable business insights.

By leveraging data science, app development becomes more effective and outcome-oriented. The growing adoption of this technology suggests a promising future.

However, given that it is still in its early stages of development, it is advisable to seek the guidance of a professional mobile app development company to ensure successful integration of data science into your app project.

Industrial IoT – 6 Manufacturing Industrial IoT Trends In 2023

Industrial IoT – 6 Manufacturing Industrial IoT Trends In 2023

The Internet of Things (IoT) involves connecting devices to form networks that collaborate with each other. This represents a shift in design from large machines to smaller, segmented ones that are easier to manage, upgrade, and maintain.

n manufacturing, the Industrial Internet of Things (IIoT) is crucial for IoT analytics to help manufacturers understand their performance and improve efficiency. Combining IIoT systems with artificial intelligence can even automate this process.

However, it is important to carefully consider how IIoT technologies can be applied to your business to remain competitive. To gain ideas about how these technologies can benefit your business, we will discuss IIoT trends in the manufacturing industry in this article.

Industrial IoT Trends

With the impact made by IOT in various industries, we will be discussing some of the IOT trends in the manufacturing industry they are as follows:

1. Connectivity Technologies

The limited amount of data that can be transmitted at once is a major challenge for IoT technology in the manufacturing industry. This is because the efficiency and speed of these systems rely on the ability to exchange data between devices in a network.

The success of real-time applications and edge computing depends on fast network speed. However, if the data does not require immediate transmission, high bandwidth may not be necessary.

Therefore, it is crucial to assess the specific requirements of your manufacturing business to determine the most suitable IoT options.

  •  Wired IIOT Solutions

If manufacturers want to connect multiple IoT devices in a network with consistent and fast solutions, wired IIoT connections are the most suitable option. These connections employ EtherCAT, Ethernet/IP, and Profinet protocols.

Although USB connections have limited range and speed, category cables can be used to extend their range. In more distant scenarios, fiberoptic cables can be used to connect industrial facilities that are miles apart. Wired solutions are more reliable and immune to noise compared to wireless technology.

However, the physical component of wiring can be a significant drawback, as it takes up space, has environmental limitations, and is time-consuming to set up. Additionally, wired connections add the extra cost of cabling.

  • Wireless IIOT Solutions

Wireless connections offer greater versatility and convenience, although more prone to inconsistency. The effectiveness of wireless connections depends on the type of connectivity used.

Bluetooth Low Energy is a more efficient option than Wi-Fi and Zigbee, making it ideal for portable and battery-powered devices. However, its data transfer capacity is limited due to energy constraints, and it is susceptible to interference in the 2.4 GHz range, which may affect device performance.

Zigbee is another alternative that works best with low-data-throughput battery-operated sensors. It depends on nodes interconnecting multiple pathways, requiring a central hub coordinator that adds to the system’s complexity.

Wi-Fi has greater potential for factory machine sensors, particularly with 5 GHz access points that provide high-speed connections for devices up to 190 feet away. Multiple beacons can be positioned throughout the factory floor to ensure maximum coverage.

This is an ideal solution for cable-powered IIoT sensors with fixed locations, as additional communications cabling is unnecessary, making setup simple. High-speed Wi-Fi connections are one of the best options for industrial Internet of Things applications in 2022.

2. Predictive Maintenance And Analytics

Costs associated with manufacturing centre malfunctions can be quite extensive. However, by utilizing artificial intelligence for predictive maintenance, companies have the potential to save millions of dollars.

For industrial machine learning algorithms to function properly, they require accurate and reliable data about the machinery being evaluated. Industrial Internet of Things sensors can collect data from a network of machines, allowing for the identification of machines that require proactive maintenance and scheduling.

These sensors can also assess machine temperature, vibration, and electricity usage, aiding in estimating future points of failure.

3. Automated Quality Assurance

Thanks to Industrial Internet of Things networks, quality assurance monitoring can be done remotely and automatically. This can improve the productivity and efficiency of manufacturing businesses greatly. Real-time alerts can be sent to allow for more rapid responses to issues like unexpected machine failures and other disruptions.

Real-time video connectivity through IIoT devices supports artificial intelligence efforts like automated visual inspection.

This allows AI to detect and remove defectives from the assembly line before they can be shipped off. AI-driven visual inspection solutions would not be possible without IoT sensors and cameras to give eyes and ears to the decision-making process.

4. Edge Computing

Edge computing is one of the most noteworthy developments in the Industrial Internet of Things technology field. Previously, many industries and businesses were inclined to shift processing from local devices to remote servers that handle data processing.

While this reduces the processing burden on local devices such as cell phones or PCs, it is time consuming and expensive in terms of bandwidth. However, the goal of edge computing is the opposite – to keep processing as close to the “edge” as possible.

In the manufacturing industry, several devices within the local edge network of a factory can handle processing without having to send data elsewhere for processing. This approach is faster, more efficient, and inherently more secure. The data never leaves the factory, eliminating any risk of interception or recovery by a third party.

Innovative industrial companies are taking advantage of new opportunities by combining edge computing and AI to create Edge AI. This concept allows AI computation to be performed near the user at the edge of the IoT network rather than in the cloud.

This brings real-time intelligence to industrial processes, increases privacy and enhances cybersecurity while reducing costs and ensuring continuous improvement of the manufacturing processes.

5. Location Tracking

The use of location tracking in manufacturing is dependent on Industrial Internet of Things technologies. GPS is effective for outdoor applications, but indoor positioning systems and areas with GPS interference, such as densely populated cities with tall buildings, pose more challenges. While logistics mostly employ outdoor GPS solutions, manufacturing focuses on indoor solutions.

Real-time location systems (RTLS) utilize wireless technologies like Wi-Fi, BLE beacons, UWB, and RFID to locate products on the factory floor. This enables monitoring of their progress from the start to the end of the production process.

This feature can aid in quality assurance verification and provide additional data to support digital twin applications.

6. Energy Optimization

Motion-sensing light switches provide us with a valuable lesson in energy optimization. Their primary purpose is to turn off the lights when a room is unoccupied rather than just making it easier to turn them on.

This concept of energy optimization extends to the Industrial Internet of Things, where we can optimize the energy consumption of other devices in our factories. For instance, we can monitor and control the temperature of our industrial machines to reduce energy usage.

The manufacturing sector is responsible for 33% of the total energy consumption in the US, according to a report by EIA. Optimizing energy consumption not only benefits the environment but can also result in significant cost savings.

By using IIoT energy optimization sensors to monitor devices’ and machines’ electrical status and usage, operators can fine-tune the process and optimize energy usage automatically. However, creating eco-friendly manufacturing processes will require more than just IoT sensors and electrical data processing.

The Future Of IoT In Manufacturing

The future success of the Industrial Internet of Things depends on various factors. Due to a chip shortage that began last year and is still affecting the market, the number of devices businesses can deploy at affordable prices is limited.

The shortage is expected to worsen with the increasing sanctions against Russia due to the recent invasion of Ukraine. Nevertheless, it is still possible to obtain and improve existing IIoT systems, and innovation is crucial during disruption.

Despite the challenges posed by the chip shortage, businesses that can find solutions with their current hardware and resources will remain competitive during and after the shortage, resulting in further gains.

To achieve your goals, it is essential to have a software development team that comprehends your requirements and vision for innovation and progress. 

Digital Marketers Challenges – 10 Top Challenges Facing Digital Marketers In 2023

Digital Marketers Challenges – 10 Top Challenges Facing Digital Marketers In 2023

The year 2020 posed various difficulties and disorderliness for everyone, including business owners who were heavily impacted by the pandemic. Due to lockdown restrictions, many physical businesses were forced to either temporarily or permanently shut down. Others had to adapt and shift their operations online.

Consequently, digital marketers in fields such as SEO, content marketing, and social media faced a surge in competition, making it harder for brands to stand out. This situation could present a problem for digital marketers.

Looking ahead to 2023, it’s likely that competition will continue to be a significant factor for online businesses and digital marketers. In order to succeed and gain recognition, digital marketers will need to focus on specific areas of their marketing strategy to outperform their competitors.

This article will explore some of the key challenges that digital marketers may encounter in 2023

Major Challenges For Digital Marketers

The following include some of the major challenges for digital marketers in 2023:

1. Learning About Your Customers

In 2023 and beyond, it will be increasingly important to focus on targeting your audience. As competition intensifies in SEO and paid advertising, it will become more crucial not to waste resources on content or keywords that are not well-targeted or too broad.

To achieve this, you can use a buyer persona, which is an effective tool to visualize and comprehend your typical customer. HubSpot’s Make My Persona is a useful tool that simplifies the process of developing buyer personas.

Additionally, you can get to know your customers better by conducting polls and asking questions on your blog and social media pages.

2. Generating Qualified Leads

In the year 2023, acquiring targeted leads will be crucial for businesses to flourish. As many service-based companies compete to gain more leads, the market is expected to become more competitive.

Consequently, paid ads like Facebook Ads and Google Ads may increase in cost. Marketers who are aware of this will use cost-effective lead generation strategies such as networking on LinkedIn, video marketing, webinars, and virtual conferences.

As the competition for lead generation intensifies, it is essential to provide significant value to prospects before closing a sale. 

3. Managing Cash Flow

In times of economic difficulty, businesses may face difficulties in managing their cash flow due to reduced spending by consumers. This can result in a prolonged period of low revenue, especially if the holiday season is not as profitable as expected.

As a result, business owners and marketers must be vigilant in managing their cash flow. This can be achieved by reducing unnecessary expenses and adopting cost-effective measures like moving their business online, which eliminates the need for renting office or retail space.

In addition, businesses with a stable customer base can explore alternative financing options such as invoice factoring.

4. Creating Engaging Content

The significance of content marketing for digital marketers will persist and evolve over time. Clients and potential customers have a constant desire for up-to-date content that educates and guides them towards a solution.

In the future, the types of content that will gain popularity include short-form videos, live-streaming content, podcasts, Instagram and Facebook stories.

Interactive content is also well-received by audiences as it provides an opportunity for individuals to share their opinions.

5. Complying With Privacy And Data-Sharing Regulations

There is an increasing trend towards implementing laws similar to GDPR worldwide, making it necessary for websites to comply with regulations of the countries they target.
It is important to be transparent about cookies, data sharing, and privacy policies on your website, and to ensure compliance with laws relating to data storage. GDPR has a specific time limit for holding onto visitors’ data.
It is also important to focus on improving email deliverability to ensure that emails are delivered to the inbox rather than the spam folder, in addition to complying with GDPR and other similar laws.

6. Making Websites Accessible

Website accessibility is an increasingly important matter that all website owners must comprehend. A website that is accessible can be utilized by individuals with various impairments such as visual or hearing impairment.

There are numerous measures you can implement to make a website accessible. For instance, including an image with alternative text to enable visually impaired users to comprehend it, which is also beneficial for search engine optimization.

Furthermore, it is crucial to ensure that users can navigate your website with just a keyboard, as not everyone can operate a mouse. The Web Accessibility Initiative (WAI) provides thorough information on web accessibility.

7. Strategizing Mobile-First

In recent times, the use of smartphones and tablets for browsing and shopping has increased significantly. Some years back, marketing specialists recommended a mobile-friendly approach, but now it is swiftly becoming a mobile-first strategy.

To take advantage of the continuously growing mobile market, it is essential to ensure that your website is optimized for mobile users. Test all features on various devices and ensure that customers can effortlessly add items to their online shopping cart, check out, and make payments using mobile devices.

Additionally, it is important to consider that large images and videos might take longer to load for mobile users.

8. Establishing An Omnichannel Marketing Strategy

Nowadays, customers are using different devices and platforms to interact with businesses. Therefore, relying solely on one strategy, such as email or Facebook, is no longer sufficient.

It is crucial to make it effortless for your customers, followers, and subscribers to reach you regardless of the platform they are on. However, this does not imply that you have to be present on all platforms. Instead, focus on the channels where your target audience is most active.

The primary goal is to provide a consistent experience across all channels, enabling people to navigate from one platform to another seamlessly. For instance, you can include links to your social media pages and website in your emails.

9. Maintaining Brand Consistency And Authority

To be perceived as a credible and dominant figure in your field, it is essential to establish a distinct brand identity that fosters customer loyalty.

In a highly competitive market, providing superior products and services is not enough. You can learn from successful brand-building companies such as Apple and Nike, whose customers are loyal and seek their products without comparing them to their competitors.

To achieve this, you must identify and highlight your unique selling point (USP) that sets you apart from the rest. Additionally, it is crucial to maintain consistency in your brand voice and style across all channels and platforms.

10. Staying Current With Google’s Algorithms

It is highly probable that Google will continue to maintain its position as the biggest search engine in 2023. If anything, Google is still expanding its digital authority.

It is imperative that businesses remain vigilant and stay up-to-date with the latest features, regulations, and algorithms that Google introduces in order to remain competitive. For companies with a local presence, they should ensure that their Google My Business listing is up-to-date and accurate.

Another crucial aspect to keep an eye on is the ranking for Google Snippets, also referred to as Position Zero in Google’s search results. The most appropriate types of content for this highly sought-after area are numbered lists and FAQs that provide solutions to customers’ most pressing inquiries.

Open Source AI Platforms – Meaning, Uses And Top 5 Of The Best Open Source AI Platforms For You

Open Source AI Platforms – Meaning, Uses And Top 5 Of The Best Open Source AI Platforms For You

Given the recent global events, it is evident that artificial intelligence has rapidly progressed, resulting in the emergence of numerous chatbots and trends.

This development has significantly impacted the software and programming industries, including robotics. As AI continues to evolve, it has introduced innovative methods of integrating software and programming into daily operations. With Open source AI this can be made possible.

Open-source AI platforms are helping humanity move toward a futuristic world faster than most anticipated.

OpenAI, Microsoft, and Google have had an outrageous month in the artificial intelligence (AI) space, and this field keeps accelerating.

What Is Open-Source AI?

To explain this term, it’s necessary to comprehend some related concepts.

Open source is a phrase used in software development which allows any programmer to collaborate on software development.

The objective is to create robust and efficient programs in a shorter time frame. This approach enables the incorporation of innovative ideas from multiple programmers to fuel technological progress. It’s similar to a group project in which humanity benefits from the team’s collective effort.

Artificial Intelligence is a field of computer science that focuses on creating programs and algorithms. These algorithms are designed to solve problems or answer queries and help machines operate more human-like.

There are several subfields of this science, including:

  • Natural language processing (NLP), which focuses on developing natural interactions between humans and computers. Specialized software helps machines process human language, create understandable words, and interact with humans through language.
  • Machine learning (ML), which prioritizes a machine’s ability to analyze information and use it to make recommendations or decisions based on the data sets it has provided.
  • Computer vision is all about creating machines that can understand and interpret visual information.
  • Robotics that can physically perform tasks without human micromanagement, including interaction with humans.

Currently, companies use image-based verification methods to confirm our human identity, where we select images with a shared feature, like cars or volcanoes. However, machines cannot still extrapolate information from these images.

Open source AI refers to software developers working together on AI projects that are accessible to the public, aiming to enhance the integration of computing and humanity.

To connect open-source AI with marketing, we can add the keyword Industry 4.0. Industry 4.0 implies that advanced computing and AI have ushered in a new age of human efficiency, highlighting the importance of utilizing open-source AI in marketing strategies.

The first industrial revolution was about creating machines to work via steam or water power.

  • The second industrial revolution was when we converted machines to electric power and embraced mass production. Products were built by human assembly lines, assisted by electric conveyor belts that brought the work to their hands.
  • The third industrial revolution was when we plugged computers into the machinery to boost efficiency and automation. Car factories now have machines programmed to quickly and precisely build cars without human assembly lines.
  • And now the fourth industrial revolution — dubbed Industry 4.0 — is about how the industry is changing now that humanity and computing are so closely interconnected. Business doesn’t just happen in boardrooms and on factory floors anymore. We carry it in our pockets.

Nowadays, we have the convenience of buying and reinvesting stocks from the comfort of our homes with just a simple click. With the help of voice-activated computers, we can even order groceries, schedule appointments on our digital calendars, and enjoy some jokes without lifting a finger.

This new interconnected technological era has paved the way for open-source AI to play a pivotal role in marketing strategies.

How Can Marketers Use AI?

AI provides various features to marketers willing to explore, ranging from minor aids to managing entire campaigns on their behalf. We will present some examples of how AI can be utilized to give you an idea of its potential.

1. Automated Social Posts

One of the ways to utilize intelligent technology in marketing, even in small ways, is to utilize software that can schedule and post pre-loaded social media content for you.

You can set the frequency of the posts, ranging from multiple times a day to once a month or more, and then load up all of your prepared content. The software will automate the process for you according to your custom schedule.

2. Content Creation

The field of AI is currently experiencing growth in its application to content creation, which is a common challenge for marketers. They face the difficulty of generating ideas that will resonate with their target audience and producing content quickly to improve conversion rates.

By utilizing AI-generated content, companies can save time and money on hiring writers and instead focus on ensuring the quality of the content produced by their experienced writers.

3. Personalized Emails And Data Capture

Many of us have likely received follow-up emails that remind us of items left in our online shopping carts or inform us of price drops on items in our wish lists. It would be impossible for humans to write personalized emails for the billions of daily online transactions, but AI can do it.

By using algorithms to analyze user data and interactions with a brand, AI can create customized email content and schedule and send it without any human intervention.

For instance, by simply saying, “Send a thank you note to Savannah,” an algorithm can retrieve Savannah’s email address from your contact list, generate a thank you email, and insert Savannah’s name into it.

The program can either send it immediately or read it to you, allowing you to make changes before sending it.

4. Ad Targeting And Pay-Per-Click Campaigns

If you use Google or Facebook for advertising, tools like AdWords provide comprehensive information and precise details to help you measure the effectiveness of your advertising campaigns. These tools also enable you to bid for pay-per-click (PPC) advertising, which allows you to allocate your advertising budget effectively.

Through AI analysis, you can identify the audience interacting with your ads and then redirect your advertising budget towards groups that may not have been considered through traditional market research. This could result in discovering more leads and conversions than expected, which can be a pleasant surprise.

Ethical Considerations Before Using Open-Source AI

Nothing new comes easy. Even the highest level of technology development has important human elements that must be addressed sooner rather than later.

1. Biases

Algorithms and datasets are created by humans with their own biases, whether they know them or not.

These biases can impact the output of artificial intelligence programs, particularly if the output is designed to change based on human behaviour in virtual environments.

One way to address this issue is to develop programs that steer clear of stereotypes and misinformation, especially when searching for bias-charged words.

Reducing bias in programming is currently a major focus in the field of AI, and solutions and regulations are still being developed to address this problem.

2. Incorrect Or Incomplete Information 

The fact that something is found on the internet or detected by AI does not necessarily imply that it is accurate or truthful. Similarly, something being widely accepted or popular does not guarantee its correctness.

Furthermore, even if the information you have is true, it does not necessarily mean that you completely understand the situation, regardless of how extensively you search for the truth using a search engine.

Open Source AI Platforms And Tools 

We have created a collection of open-source AI tools to present you with top choices to consider as you explore this area.

You can use this list to determine whether adopting micro AI could enhance your ROI or if bigger open-source AI projects are more appropriate for achieving your company’s objectives.

1. TensorFlow

TensorFlow is a comprehensive framework that facilitates collaboration among programmers to develop innovative projects and leverage existing models created by other experts.

It is a powerful AI platform that provides training videos to assist you in getting started. success.

What we like: This platform supports several programming languages, including Swift, Python, and JavaScript — the most common programming language used on Earth.

Price: Free.

2. PyTorch

Like TensorFlow, PyTorch is a complete platform designed to help transform concepts into effective applications. It is a comprehensive framework created to support various aspects of open-source AI project development, providing vast libraries and datasets to utilize.

PyTorch is user-friendly for developers who are already familiar with coding in Python. The platform’s object-oriented approach assists in bundling up usable code chunks that perform a single task.

These reliable “objects” can be integrated into more complex sequences to accomplish more complicated tasks, promoting collaboration among programmers.

Pro tip: Programmers fluent in Python flourish here, but it also has a C++ interface for those who don’t code with Python.

Price: Free.

3. Keras

Keras is an API that is geared towards human use, enabling the sharing of the front end of deep learning models quickly and straightforwardly.

This interface allows for the easy exportation of models from Keras, which can then be utilized across various platforms such as browsers, iOS, and Android. The Python libraries offered by Keras tend to concentrate on artificial neural networks..

Best for: Programmers who prefer a more streamlined user interface while working with the newest versions of TensorFlow, simplifying interaction with the software as it’s being built.

Price: Free.

4. OpenAI

OpenAI has been making headlines due to its revolutionary impact on natural language processing (NLP) AI programs. One of its noteworthy models, Codex, enables the conversion of natural language into code in a specific programming language of your choosing.

Additionally, similar to other open-source AI projects, OpenAI’s models are available for user customisation. OpenAI is not only advancing the capabilities of AI assistants like Alexa and Siri but also taking the next step in Industry 4.0.

This AI can generate its own natural language responses by synthesizing information instead of merely directing users to a website and reading the answers aloud. This is truly remarkable progress, and you have the opportunity to collaborate with it!

Price: Free $18 credit to experiment for three months, then prices are token-based and depend on what you use as you go.

5. OpenCV

OpenCV is a popular open-source AI platform that specializes in computer vision. While TensorFlow can be considered a general AI undergraduate degree, OpenCV has a master’s in AI vision.

Its library was written in C, which makes it highly adaptable and can be used on various platforms, from PowerPC Macs to robotic dogs.

OpenCV boasts a new C++ interface, and developers have created wrappers for other languages, such as Java and Python, to facilitate cross-language development.

Best for: Developing AI specifically for computer vision applications.

Price: Free, including for commercial use.

Load More

Back to top