Trending December 2023 # Ai Technology Skills Highest In Demand In 2023 # Suggested January 2024 # Top 19 Popular

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Technical skills that are in greatest demand today are analytics, user experience, automation, IT architecture and artificial intelligence, revealed new research from Infosys NSE 0.99% on Thursday.

Infosys looked at two perspectives on barriers that prevent an adequate demand-supply match for talent – concrete and intangible barriers to talent transformation.

Among the substantial barriers, absence of budget is the greatest obstruction to repurposing endeavors, trailed by hierarchical issues, absence of management mindfulness or support, and lacking ability management plans.

Among the impalpable barriers, learnability got quick work in the examination, and it restrains the colossal capability of reskilling as an upper hand for companies that grasp learnability. Underestimating learnability constrains the capability of other ability activities.

Related: – Success of Artificial Intelligence its Secret

“There isn’t only an ability war – it’s starvation. To succeed, companies must contract, create, and hold ability superior to anything their opposition,” said Pravin Rao, Chief Operating Officer, Infosys.

By way of instance, the soft ability empathy is vital for designing thinking, whilst learnability is essential to the continuous technical and personal development necessary for digital ability.

According to a study, the Infosys Knowledge Institute conducted an online survey and analytics that included more than 1,000 CXOs and other senior-level respondents from companies with revenue more than $1 billion.

Respondents represented multiple industries and were from India, China, Australia, France, Germany, the UK and the US.

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Top 10 Cryptocurrencies With Highest Market Capitalization In 2023

Cryptocurrencies’ market capitalization varies based on the price of the underlying trends.

There are hundreds of Cryptocurrencies’

Top 10 cryptocurrencies with the highest market capitalization Bitcoin

Market Capitalization: US$615.61 billion Bitcoin, the household name that became the synonym of cryptocurrency, was created with a motive to decentralize distribution, trade, and storing. The bitcoin market is highly volatile with sudden surges and plummets. In 2023, the digital currency was traded at US$20,000. Just in a year, the price fell to half of its previous value. Despite its instability, Bitcoin has made a remarkable stance in the cryptocurrency market and also inspired crypto investors to try their hand on other cryptocurrencies. Unfortunately, Bitcoin’s recent market trend is not impressive. Following Elon Musk’s decision to not accept Bitcoin at Tesla and China’s crackdown on Bitcoin mining, the digital currency is hitting the ground hard. However, crypto enthusiasts say that this is a temporary fall of Bitcoin and it will return with an even stronger ground in the cryptocurrency market.

Ethereum

Market Capitalization: US$290.32 billion Ethereum, supported by the Ethereum platform, is a blockchain-based cryptocurrency. Digital currency is considered a safe place to invest compared to other cryptocurrencies because of the blockchain platform that handles it. Ethereum is a decentralized public ledger for verifying and recording transactions. In the current market scenario, Ethereum is having a tough fight with Bitcoin. Following the fall of Bitcoin, Ethereum is getting a stronghold in the digital currency market. The digital currency’s 10-day average trading volume has doubled toward 80% of Bitcoin’s from the start of 2023. Crypto enthusiasts predict that it won’t be long until Ethereum takes over Bitcoin in market capitalization.

Tether

Market Capitalization: US$62.32 billion Tether, a blockchain-based cryptocurrency, is backed heavily by an equivalent amount of traditional fiat currencies like the dollar, the euro, or the Japanese yen, which are held in a designated bank account. Formerly known as Real coin, Tether was launched by Tether Operations Limited in 2014. Unlike many cryptocurrencies, Tether is designed as a stablecoin that is attested to real-life assets or commodities to ensure stability in value and lower volatility in the market. Even though the digital currency saw wide purchase and exchange over the past few years, the questions on its reserves with the US dollar remain unanswered. Recently, Tether has extended its wings into Avalanche blockchain.

Binance Coin

Market Capitalization: US$53.39 billion Since the beginning of 2023, Binance Coin saw a drastic surge in value. The digital currency has put itself at the forefront as Bitcoin and Ethereum’s challenger. Remarkably, following the attraction that Binance Coin has got, many crypto investors also bought Binance Coin to try their hand on the fourth-largest cryptocurrency by market cap. However, that doesn’t make the digital currency break its US$433 record. Elon Musk and Bitcoin’s break-up also had a negative impact on Binance Coin, dropping the cryptocurrency’s price by 15% to US$365.48 this week. Fortunately, the digital currency also bounced back to its rate immediately and is maintaining stable ground.

Cardano

Market Capitalization: US$48.88 billion Cardano, the proof-of-stake blockchain currency, shares characteristics and applications with other blockchain platforms like Ethereum. The altcoin has been performing well since last week. Recently, big crypto investors also picked Cardano as their top choice, thanks to all the promising developments that the cryptocurrency has made in the past months. Cardano core team is looking to join the functional ranks of Ehterum and other competitors with its planned implementation of smart contracts. Even though the coin is priced at just US$1.85, its value surged over ten times since the beginning of 2023.

Dogecoin

Market Capitalization: US$41.90 billion

XRP

Market Capitalization: US$39.53 billion XRP is a cryptocurrency that runs on the XRP ledger, a blockchain engineered by Jed McCaleb, Arthur Britto, and David Schwartz. Unfortunately, digital currency is not performing well in the market. Recently, in a single day, Ripple’s price fell by 10.77%, marking the largest one-day surge since its inception. This has eventually brought down the market cap by 2.60%. Ripple is also on a tough court fight with the US Securities and Exchange Commission (SEC) to prove that XRP sales are beyond the jurisdiction of the US watchdog.

USD Coin

Market Capitalization: US$23.04 billion USD Coin (USDC) is a stable coin pegged to the value of US$1, so it makes it easy to sell users’ crypto assets for virtual fiat currencies. Even though the digital currency’s price is not appealing, its stability and coordination with the market rate make it worth a try. Recently, Visa announced its plans to settle transactions in USDC on the Ethereum blockchain. Today, many crypto investors use USDC to interact with decentralized finance (DeFi) programs.

Polkadot

Market Capitalization: US$19.96 billion Polkadot is a recently launched cryptocurrency that made it into the top 10 list. Created by Ethereum’s co-founder, Gavid Wood, in 2023, the digital currency expands on the functionality of its predecessors with the goal of building a decentralized internet. Compared to Bitcoin and Ethereum that can process roughly three transactions per second, Polkadot can process more transactions in parallel. Recently, an Asian blockchain incubator and venture capitalist announced that it had launched a US$30 million venture capital fund called Master Ventures Polkadot VC Fund.

Uniswap

Market Capitalization: US$13.40 billion

Why Is Python So In Demand In The Machine Learning And Ai Fields?

For Machine Learning and Artificial Intelligence, Python has emerged as a well enough and efficient high-level programming language. Data scientists, academics, and developers in various fields embrace it as their language of choice. What, though, makes Python such a perfect combination for these applications and research areas? We’ll analyze Python’s significance in the machine learning and AI disciplines in this article.

The Top Seven Reasons for Python’s Popularity in AI and Machine Learning Ease of Use and Readability

Python is an easy-to-read and gaining knowledge of programming language, making it perfect for novices. Its simple syntax and readable, herbal language fashion make it easy to write and comprehend, enabling builders to produce comprehensible code quickly and effectively. This is especially important in the field of computers gaining knowledge of artificial talent (AI), the place where code can be very complicated and challenging to understand. Working with these kinds of apps is a tremendous suit for Python due to the fact of its simplicity and readability.

Large Community and Resources

Python has a large and bright developer community that is usually developing new machines and gaining knowledge of artificial intelligence frameworks, libraries, and tools. This suggests that programmers have access to a huge variety of tools, consisting of open-source initiatives, tutorials, and documentation, that can enhance their development processes, hasten time-to-market, and make it simpler to address difficult problems.

Availability of Powerful Libraries

For working with data and growing AI and ML models, Python comes with a variety of modules that have proven to be quite helpful. NumPy and Pandas are two of the most famous libraries due to the fact they supply useful information structures and tools for manipulating and examining data. Additionally, Scikit-Learn is a vital library that offers a wide selection of machine learning and statistical modeling algorithms.

Flexibility and Versatility

Scientific computing, web development, data processing, and visualization are just a few of the many uses that Python’s versatility makes possible. In the disciplines of machine learning and AI, where developers routinely work with various types of data and models, this adaptability is particularly essential. Python is a flexible alternative for challenging tasks because it also allows for simple integration with other programs and languages.

High-Performance Computing

Although Python isn’t recognized for being fast, there are ways to improve its efficiency. Utilizing tools and libraries made especially for this purpose is one such method. For instance, just-in-time compilation can be used with Numba to speed up Python programming. Another choice is PyPy, a different Python implementation that offers superior performance. Additionally, Python can be used in conjunction with other high-performance computing equipment like C++ and CUDA to similarly increase performance.

Support for Deep Learning Frameworks Increasing Industry Adoption

In addition to the aforementioned reasons, the enormous use of Python in the AI and ML fields can also be attributed to its growing adoption by using leading enterprise players. Big names like Google, Facebook, and Microsoft have developed their very own Python-based equipment and libraries, which has in addition pushed its growth in the field. As a result, authorities who are well-versed in Python are in excessive demand and can obtain a range of job opportunities.

Conclusion

In summary, Python has earned a reputation as the programming language of preference for professionals in computers gaining knowledge of artificial intelligence. Its large community and ecosystem of libraries, along with its simple syntax and readability, make it a reachable and adaptable tool for working with difficult records analysis and deep learning. Python is projected to preserve its dominance in the place as demand for laptop learning and AI rises, making it a vital device for companies making an attempt to use records for increase and innovation.

6 Ways Technology Can Boost Productivity In 2023

Are you looking for ways in which technology can increase productivity in your business? Here’s your answer.

Are you looking for ways in which technology can increase productivity in your business?

High productivity in business is important because it results in greater revenue generation and better customer service. When your staff is productive, they will invest all their efforts into helping your business grow.

Technology can help you improve productivity in your business in many ways. For example, you can automate various repetitive processes, use scheduling software, and install a chatbot to answer customer inquiries. All of this provides you with time to spend on more important things, such as brainstorming marketing strategies.

This article will show you 6 ways in which technology can boost productivity in your business.

1. Use Video Conferencing Software

Video conferencing has risen in popularity ever since the start of the pandemic. And it has brought with it many benefits to businesses. People from different parts of the world can now meet through video calls and strike huge business deals.

As a business, you can now save many expenses. In the past, you needed to pay people to fly in, and pay for their hotel rooms, and conference halls. Video conferencing software relieves you of these expenses. Nowadays, you can hold meetings online and make critical decisions without being in the same room.

Video conferencing software also saves you the time you would have needed to travel to the meeting. Once you prepare the details, you can just hop into the call in a quiet room. Once you’re done, you can get back to doing your other tasks.

2. Use proxies for research

Market research is an unskippable part of any successful business strategy. It helps you learn your target audience’s pain points and desires. You also discover the strengths and weaknesses of your competitors.

Most companies do research manually, and this can be time-consuming. But with proxies, you can collect large amounts of data by scraping millions of websites simultaneously. You can then analyze this data to identify patterns and reach relevant conclusions that can impact your business decisions.

3. Use a scheduling system

A scheduling system helps you plan your days, so you have ample time to interact with employees. You won’t need to worry about overlapping appointments, events, and meetings you have going on. When everyone in the company can see your calendar, they can fix theirs to match your free time if they want to meet.

Scheduling systems also boost teamwork by enabling you to assign tasks to teams with a set of instructions. Apps like Asana allow you to schedule tasks that should be completed by people in your organization.

4. Use Virtual Private Networks

VPNs allow employees to work outside the office securely. This technology boosts employee productivity by leveraging internet resources so employees can collaborate from different locations, such as from home. The result is a faster, more secure network environment where users can share data and communicate safely.

VPNs also allow employees to access sites that may be useful to your company but are region-specific. For example, if one of your employees is working from Asia and one of the sites she wants to access is only available if you’re in the US and Europe, a VPN will do the magic.

5. Outsource work

In a startup, almost everyone will do an extra job they were not hired to do because you are trying to cut the cost of bringing in new employees. But what if that’s holding their productivity back?

By outsourcing work your employees are not qualified to do, you will boost your performance. They can focus on essential tasks that grow your business while outsourcing repetitive tasks. If you don’t understand IT, you can hire an expert to save you the time you would spend messing up trying to figure out how things work.

6. Use chatbots and FAQ pages

Chatbots and FAQ pages can improve your customer service by quickly providing information your customers need. Instead of calling your business, they just visit your site and go through the FAQ pages.

Chatbots can also help your business outside office hours. They can provide answers to queries your customers or prospects may urgently want to know.

7. Clear your mind with mindfulness apps

If you’re having trouble with productivity, chances are you have too much on your mind, and you keep jumping from one thought to another. You’d be surprised to see how much something as simple as meditation can help.

Mindfulness applications have a bunch of useful guided meditations and other mindfulness techniques that will help you in ways no other digital tools can.

Conclusion

Business productivity has many benefits, including employee fulfillment, better customer service, increased employee engagement, and better revenue generation. Technology can help you achieve all these benefits in many ways, so don’t miss out on any of the useful software listed above!

Top 4 Use Cases Of Ai In Fashion In 2023

Like every other sector, AI is also changing the fashion industry by offering solutions to various challenges. The global market for AI in the fashion sector was reported at $270M in 2023 and is projected to grow to $4.4B by 2027.

This article explores top AI use cases in the fashion industry to help business leaders in the sector learn where AI can be implemented in their businesses.

AI for design

Most companies in the fashion sector rely on clothing designs made manually. However, creative AI can be an effective way to take over in situations like the pandemic when people can not work. AI-enabled tools can create clothing designs by using data such as images from the brand’s previous offerings or from other designers, data regarding customers’ tastes (color and style choices), and current fashion trends.

Watch this video to see how the London college of fashion, amongst other institutions, is researching to find new ways to use AI for fashion design and production:

While extensive research is being done in this area, limited real-world applications of AI-enabled fashion designing can be observed, and all of the ones that exist are based on human-in-the-loop (HITL) models.

For example, the German fashion platform Zalando and Google created project Muze, which uses machine learning to create fashion designs. The model gathers data regarding customers’ favorite textures, colors, and style preferences by asking a series of questions to create clothing designs.

The project created 40,424 fashion designs within the first month.

However, some found the designs created by the model strange and unwearable (See the image below).

But, with generative AI growing and improving at the speed of light, the design will soon be practical and considerable.

Improved production

Currently, the apparel manufacturing sector mostly relies on manual production processes with questionable working conditions for the workers. However, AI-enabled solutions are changing these trends by enabling automation in the apparel production sector.

AI can help overcome these ethical challenges by enabling automation. For instance, robotics can help automate risky or error-prone tasks in a manufacturing facility to decrease workload and improve worker safety. Companies like Sewbo and Softwear are revolutionizing clothing production by developing automated garment-producing machinery.

Moreover, computer vision enabled with AI also has various applications in fashion production, including efficient quality assurance and predictive maintenance of equipment which reduces the downtime of the machines and ensures operational continuity.

For more on AI training data collection, feel free to download our free whitepaper:

Trend forecasting

Fashion trend forecasting is the process of predicting possible future fashion trends. Traditionally, fashion trend forecasters combine their fashion knowledge, intuition, and historical data to predict possible fashion trends. However, measuring the accuracy of trend forecasts is difficult, and you can not know how accurate they are.

In the current digital era, AI is being used to accurately predict fashion trends using different types of data. For instance, the fashion tech company Heuritech developed an AI-enabled service to predict fashion trends by analyzing millions of social media images.

Watch the video to learn more:

Trend prediction can also be used to reduce wastage in the fashion and clothing sector by designing clothes people would actually want to wear. More accurate predictions can lead to leaner production and distribution cycles and less waste.

Improved fashion retail

AI-enabled technologies are widely used in fashion retail. The applications include:

Intelligent automation of repetitive back office tasks such as invoice creation can be automated.

AI-enabled computer vision systems can enable inventory management automation, retail theft prevention, cashierless automated stores, etc.

RPA also has various applications in retail, including improved customer relationship management and marketing operations.

Watch how H&M, one of the largest fashion retailers in the world, leverages AI to improve its operations:

Exploring Analytics & Ai In 2023: A Detailed Primer

It is often said that data is the most valuable asset a business can have; the oil of a digital era. But data itself, while interesting, often leaves out a variety of important details – creating a need for analytics. And how we complete these analytics has evolved – and will continue to do so; particularly with the rapid proliferation of AI tools and technologies.

What is analytics? [Separate article]

What are the benefits of analytics?

Data analytics allows companies to institute data-driven decision making which allows companies to minimize bias and gut feel while making both important corporate decisions and making fast operational decisions. For example; with Data Analytics, a business can classify customers according to patterns found in data to understand their customers’ perspective.

Additionaly, a successful analytics program can lead to:

Increased productivity via instant access to the right data through better classification processes

Improved speed, accuracy, and efficiency throughout analytics and data management practices

Facilitating clear compliance with legal and similar requirements

Cost saving by finding the best ways to achieve a wide range of tasks

To better understand data, analytics, AI, and how they all go together, we will answer the following questions:

Why is analytics important now?

The belief that data can serve as a guide is growing stronger in both business and government. Organizations want to act on the growing data sets they are gathering, which lead them to place analytics at the center of the decision-making process.

Source: ArcadiaData

How did analytics evolve?

Source: IIA

Analytics 0.1 Analytics 1.0 Analytics 2.0

In the early 2000s, the volume of data generated from individuals exploded as the internet became a mainstream product. This is called the era of Big Data because of the amount of data provided from phones and social media platforms. Big Data technologies and tools enlarged the set of capabilities that data analytics can do. Some of these capabilities are processing data and leveraging with code in open-source software like Hadoop.

Analytics 3.0

With further increase in data generated and increased expectation to make real-time decisions, decision making needs to be pushed to edge devices. Analytics 3.0 combines the information generated at the edge with internal data of the organization using the latest machine learning approaches to predict potential outcomes.

Shankar Meganatha, Oracle Enterprise Cloud Architect, has a simple explanation for Analytics 3.0:

Every device, shipment, and consumer leaves a trail referred to as data exhaust. It’s just not feasible to keep moving very large amounts of raw data to centralized data stores – it is too big, changing too fast, and hyper distributed. Analytics 3.0 is essentially a combination of traditional business intelligence, big data and Internet of Things (IoT) distributed throughout the network.

What are the trends shaping analytics today?

Gartner’s hype cycle shared at the top of the article and our interviews highlighted these trends:

AI is helping analytics get automated, more accessible and more powerful. We have explored this in detail in our AI in analytics article.

Analytics Enablers

Natural language user interfaces (NLUI): All employees need to be able to at least access analytics. These interfaces enable employees to write their queries in natural language and access results,

Application specific analytics

Text analytics: Analyzing text requires the use of Natural Language Processing (NLP), a branch of AI. There are emerging specialized analytics tools for analyzing written communication.

What are the best practices for analytics?

Based on analytics vendors and our own experience and interviews, these are emerging best practices:

High quality data collection is an important first step

Most organizations still throw away a significant share of their data or keep data with significant quality issues. With data issues, analytics can be an exercise of garbage in, garbage out.

To see the big picture in your data, it is good to use the most suitable visualization tools. Visualization is a great way to understand and present insights in the process of decision-making.

Identify your KPIs and regularly monitor them via dashboards

Identifying and tracking KPIs is critical for any business. To achieve this, it is good to keep in mind the end goal of the collected data. With that answer, you can add relevant parameters to your reports or dashboards, and remove the redundant ones. Well-prepared reports and dashboards, which are free from irrelevant metrics, eases organizations’ understanding of the current situation and improvement areas.

Check your Analysis Periodically

Results of your analysis will change as your company and market conditions evolce. A decision your organization makes today may not be the most optimal decision tomorrow. That’s why periodical analytical checks of the insights can be a life-saving action.

What are the analytics pitfalls to avoid?

We have heard about these analytics pitfalls as we spoke to practitioners:

Organizations that operate on executives’ gut feel rather than data. First step in setting up analytics in an organization should be to ensure that the whole company, from management to most junior employees, follows data-driven decision making.

Operating a small sample size of data, which is associated with low statistical power, prevents organizations from success. Make sure you have a large volume of data to get accurate insights.

Data may give you all the information you need, but analysts should be careful while translating it into insight. Confusing causation with correlation may lead to misleading conclusions, be hesitant to make conclusions and try to find out an answer to the question of why.

Some tools that can enable more powerful analytics include:

NameFoundedStatusNumber of Employees Amazon Machine Learning1994Public10,001+ Ayasdi2008Private51-200 BigML2011Private11-50 H2O.ai H2O2012Private51-200 IBM Watson1911Public10,001+ Infosys1981Public10,001+ Microsoft Azure Machine Learning1975Public10,001+ Receptiviti2023Private11-50 SAS Analytics Suite1976Public10,001+

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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