“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and AI. “
source : www.lebigdata.fr
The document discusses several emerging technologies and their predicted impacts, according to analyst firm Gartner. It provides assumptions from Gartner about the growth of artificial intelligence, autonomous vehicles, blockchain, and augmented reality shopping by certain dates. It also discusses technologies like the Internet of Things, big data, business analytics, data mining, and disruptive technologies such as blockchain, virtual/augmented reality, Industry 4.0/smart manufacturing.
The document discusses various topics related to artificial intelligence including health passports, embedded AI, responsible AI, generative AI, AI-augmented development, autonomous vehicles, blockchain, edge computing, augmented reality shopping, and the increasing use of virtual assistants. It also provides several predictions about the growth and impact of AI and related technologies by 2022 and 2030.
The document discusses how the rise of the Internet of Things (IoT) will change product roadmaps. It notes that IoT will result in more smart, connected devices generating large amounts of fragmented data from multiple sources. This will require applications to become smarter by incorporating predictive and prescriptive capabilities to leverage IoT data. Challenges also need to be overcome, such as handling big data, privacy, and security issues. However, properly leveraging IoT data through smarter applications can provide significant financial benefits and opportunities for innovation across industries.
AI in Business - Key drivers and future valueAPPANION
Artificial Intelligence is undoubtedly a hyped topic at the moment. But what is the reasoning for investors and digital platform players to bet very large amounts of money on this technology right now? To better understand the current market dynamics and to give an overview of renown predictions for the upcoming 2-3 years, we compiled a practical overview of this topic. This report covers the major driving forces of AI, assumptions for the future from the industry thought leaders as well as practical advice on how to start AI projects within your company.
The document discusses the nature of data driven service innovation. Some key points made include:
- Data driven service innovation aims to create new services by finding innovative uses of data, but this process is messy and experimental as requirements are poorly defined initially.
- Big data projects resemble research more than production, requiring agility to combine conventional project management with an ability to fail fast and learn from mistakes.
- The complexity of modern ICT systems makes perfect causal understanding impossible. We must acknowledge our ignorance and use a probe-sense-analyze-act approach.
- Developing services is challenging as the customer experience is co-created and hard to define formally. Operational staff closest to customers provide important insights.
This document discusses the shift from Big Data 1.0 to Big Data 2.0. Big Data 1.0 focused on introducing technologies like Hadoop to take advantage of new data sources but faced challenges of complexity, specialized skills requirements, lack of security/availability, data skills shortage, and performance issues. Big Data 2.0 will see shifts like cooperative processing across platforms, accessible analytic tools for non-experts, moving processing to data for real-time analytics, combining relational and non-relational data, abstracting infrastructure complexity, and unified platforms covering the entire analytic process to unlock over $15 trillion in untapped value from data. Companies that embrace these Big Data 2.0 capabilities can achieve better performance, faster
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
The document discusses the opportunities and challenges presented by the Internet of Things (IoT). It describes how IoT allows devices and sensors to connect and share data, enabling new applications and services. The IoT market is estimated to be worth $1.9 trillion by 2020. While IoT presents opportunities, effectively managing the vast amounts of diverse data from numerous connected devices is challenging. A proven platform is needed to securely acquire, integrate, analyze and act on IoT data to create business value from this technology.
The document discusses several emerging technologies and their predicted impacts, according to analyst firm Gartner. It provides assumptions from Gartner about the growth of artificial intelligence, autonomous vehicles, blockchain, and augmented reality shopping by certain dates. It also discusses technologies like the Internet of Things, big data, business analytics, data mining, and disruptive technologies such as blockchain, virtual/augmented reality, Industry 4.0/smart manufacturing.
The document discusses various topics related to artificial intelligence including health passports, embedded AI, responsible AI, generative AI, AI-augmented development, autonomous vehicles, blockchain, edge computing, augmented reality shopping, and the increasing use of virtual assistants. It also provides several predictions about the growth and impact of AI and related technologies by 2022 and 2030.
The document discusses how the rise of the Internet of Things (IoT) will change product roadmaps. It notes that IoT will result in more smart, connected devices generating large amounts of fragmented data from multiple sources. This will require applications to become smarter by incorporating predictive and prescriptive capabilities to leverage IoT data. Challenges also need to be overcome, such as handling big data, privacy, and security issues. However, properly leveraging IoT data through smarter applications can provide significant financial benefits and opportunities for innovation across industries.
AI in Business - Key drivers and future valueAPPANION
Artificial Intelligence is undoubtedly a hyped topic at the moment. But what is the reasoning for investors and digital platform players to bet very large amounts of money on this technology right now? To better understand the current market dynamics and to give an overview of renown predictions for the upcoming 2-3 years, we compiled a practical overview of this topic. This report covers the major driving forces of AI, assumptions for the future from the industry thought leaders as well as practical advice on how to start AI projects within your company.
The document discusses the nature of data driven service innovation. Some key points made include:
- Data driven service innovation aims to create new services by finding innovative uses of data, but this process is messy and experimental as requirements are poorly defined initially.
- Big data projects resemble research more than production, requiring agility to combine conventional project management with an ability to fail fast and learn from mistakes.
- The complexity of modern ICT systems makes perfect causal understanding impossible. We must acknowledge our ignorance and use a probe-sense-analyze-act approach.
- Developing services is challenging as the customer experience is co-created and hard to define formally. Operational staff closest to customers provide important insights.
This document discusses the shift from Big Data 1.0 to Big Data 2.0. Big Data 1.0 focused on introducing technologies like Hadoop to take advantage of new data sources but faced challenges of complexity, specialized skills requirements, lack of security/availability, data skills shortage, and performance issues. Big Data 2.0 will see shifts like cooperative processing across platforms, accessible analytic tools for non-experts, moving processing to data for real-time analytics, combining relational and non-relational data, abstracting infrastructure complexity, and unified platforms covering the entire analytic process to unlock over $15 trillion in untapped value from data. Companies that embrace these Big Data 2.0 capabilities can achieve better performance, faster
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
The document discusses the opportunities and challenges presented by the Internet of Things (IoT). It describes how IoT allows devices and sensors to connect and share data, enabling new applications and services. The IoT market is estimated to be worth $1.9 trillion by 2020. While IoT presents opportunities, effectively managing the vast amounts of diverse data from numerous connected devices is challenging. A proven platform is needed to securely acquire, integrate, analyze and act on IoT data to create business value from this technology.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
The document discusses how the Internet of Things (IoT) will reshape businesses by connecting previously offline processes and products online. It will require companies to establish direct, always-on connections to customers and fundamentally change how they operate, interact with customers, and generate revenue. The challenges of building successful IoT businesses include launching connected services and products, managing them ongoing, and monetizing the connected services through complex billing relationships, cost controls, and data-driven decisions. Service IT provides the technologies and capabilities needed to address these challenges and enable companies to capitalize on the opportunities of IoT.
This document discusses how Encanvas can help businesses leverage IoT and big data. Encanvas manages IoT assets and aggregates their performance data with other sources to generate new insights. It gathers, analyzes, and interprets diverse data in new ways to produce value and new business approaches. Encanvas provides a platform to build scalable applications that connect to various data sources, perform extract-transform-load processes, and automate tasks using robotic functionality.
Data science provides businesses with advanced tools and technologies that allow them to automate complicated business processes linked with extracting, analyzing, and presenting raw data.
With so much happening in the technical field, and the data being generated at a rapid speed, it is crucial to know about the latest as well as the upcoming trends in data science.
This document discusses Oracle's Internet of Things platform for connecting machines and devices. It describes how Oracle provides a complete solution to develop and deploy applications across devices and data centers, manage and analyze large volumes of machine-generated data, integrate device data with enterprise applications, protect data through all stages of processing with security and compliance capabilities, and optimize business operations and innovation with Oracle applications and engineered systems.
The document provides an overview of digital marketing and e-commerce trends. It discusses key concepts such as digital marketing, types of e-commerce like business-to-business and business-to-consumer, the growth of mobile commerce, and social media marketing platforms like Facebook and Twitter. Examples of typical marketing campaigns on Facebook and Twitter are also summarized.
This presentation was done for our term paper where we went to two organizations namely Shwapno and The City Bank ltd of Bangladesh and conducted survey on the employees about how they were using their information systems and how much comfortable they were. a brief discussion of information systems is provided here with a discussion on nformation systems in perspective of Bangladesh.
The Internet of Things is an emerging topic of technical, social, and economic significance. Consumer products, durable goods, cars and trucks, industrial and utility components, sensors, and other everyday objects are being combined with Internet connectivity and powerful data analytic capabilities that promise to transform the way we work, live, and play. Projections for the impact of IoT on the Internet and economy are impressive, with some anticipating as many as 100 billion connected IoT devices and a global economic impact of more than $11 trillion by 2025.
This document discusses how businesses can transform by gaining insights from data collected by internet-connected devices and sensors. It explains that the Internet of Things allows physical objects to connect, share data, and interact. While much data from connected devices is unused, businesses can analyze this data using advanced analytics to gain insights, optimize operations, innovate new products and services, and improve customer engagement across all industries. The document outlines IBM's approach to helping businesses capture opportunities from the Internet of Things.
Digital Futures Webinar with Amaze CSO Rick Curtis Jan 2014amazeplc
Amaze's Chief Strategy Officer, Rick Curtis gives his thoughts on what will be hot in digital over the next 24 months and beyond. Rick discussed the following areas:
• The trends that will change the way businesses function and how we live our lives through 2014 and beyond
• What businesses should be thinking about as they compete in the continually evolving technological landscape
• What businesses need to be doing in order to remain competitive in a future being driven by Connectivity, Context and Collaboration.
To view a recording of this webinar, please click here:
https://vimeo.com/amazeplc/review/83694979/18dc31fd96
The document discusses findings from a survey conducted by GE and Accenture on how industrial companies are approaching and investing in Big Data analytics and the Industrial Internet. Key findings include:
1) Executives see Big Data analytics as critical and are investing over 20% of technology budgets in it, expecting investments to increase.
2) Board-level support is the primary driver of Big Data strategies over other executives.
3) Companies feel a sense of urgency to implement Industrial Internet solutions as 84% believe it can shift competitive landscapes in a year and competitors are leveraging it.
4) Companies are moving beyond basic asset monitoring with Big Data to optimize operations and create new revenue through predictive maintenance, efficiency gains
Should I Choose Machine Learning or Big Data?Bernard Marr
Big Data and Machine Learning are two exciting applications of technology that are often mentioned together in the space of the same breath. In reality, there are important distinctions that need to be understood when we are making decisions about our business data strategy.
Top 10 Technology Predictions - Future Outlook for AI and DLTAPPANION
Artificial Intelligence and Distributed Ledger Technology are the current hot topics on the innovation agenda. In our future outlook for 2019 and beyond, we made 10 bold predictions for the upcoming development of these two key technologies.
The Human Side of Artificial IntelligenceBernard Marr
Sometimes it's easy to forget that humanity is the most important element of artificial intelligence (AI). AI is created by humans to solve human problems and to do so by emulating our own abilities to think, learn, and improve. A new Microsoft initiative aimed at broadening the understanding – and assuaging the fear – of AI has been developed with this important fact in mind.
"Data Science is highly resourceful when it comes to an understanding of the public and their decisions for service providers' products and services. The top 8 exciting trends that our world will be able to see in Data Science in the coming year of 2021 are discussed here. "
The New Global AI Arms Race: How Nations Must Compete On Artificial IntelligenceBernard Marr
As governments race to unlock AI’s potential within their countries, who is leading the pack, and who is falling behind? Get the latest research and advice about what it takes to get an advantage in the global AI arms race.
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
This white paper discusses how organizations can transform big data into business value by connecting various data sources, analyzing data at scale, and taking action. It outlines the challenges of dealing with exponentially growing data in today's digital world. The paper introduces Actian's solutions for enabling an "action-driven enterprise" through its DataCloud Platform for invisible integration and ParAccel Platform for unconstrained analytics. These platforms allow organizations to connect diverse data, analyze it without constraints, and automate actions based on insights gleaned from big data analytics. Use cases demonstrate how companies are leveraging Actian's technology to gain competitive advantages.
The document discusses how the rise of the Internet of Things will require organizations to adapt their corporate structures and executive roles. As IoT connects more devices and generates unprecedented data, executives must work together across functions like operations, technology, information, marketing and human resources. The CEO must recognize opportunities in big data while the CIO manages vast information flows. As privacy and security challenges emerge, the CSO and CLO must collaborate to ensure compliance. Overall, success in the IoT era will depend on innovation through interdependent relationships between C-level executives.
What Are The Latest Trends in Data Science?Bernard Marr
The benefits of a data-driven approach to business are well established but not set in stone. The relentless march of technological progress means the boundaries of what is possible are constantly being redrawn, spawning new behaviors, trends and buzzwords.
From hype to action getting what's needed from big data agwdeodhar
The document discusses the challenges companies face in realizing value from big data analytics. While big data holds potential for competitive advantage, most companies still struggle with managing vast amounts of data from various sources and finding ways to gain useful insights. Early adopters have found success, but full adoption of big data analytics remains limited due to challenges like lack of skills and understanding how insights can impact organizations. The document argues that in order to benefit, companies need solutions that easily manage the entire data workflow and provide insights to business users in a self-service manner.
Analytical Thinking is a fortnightly newsletter from the UK Business Analytics team.
The purpose of the newsletter is to raise awareness about why analytics is a hot topic at the moment, where is analytics being referenced in the press and in what ways are organisations using analytics.
Business Analytics (Operational Research) is part of the Digital Transformation team in Capgemini Consulting UK
The document discusses how the Internet of Things (IoT) will reshape businesses by connecting previously offline processes and products online. It will require companies to establish direct, always-on connections to customers and fundamentally change how they operate, interact with customers, and generate revenue. The challenges of building successful IoT businesses include launching connected services and products, managing them ongoing, and monetizing the connected services through complex billing relationships, cost controls, and data-driven decisions. Service IT provides the technologies and capabilities needed to address these challenges and enable companies to capitalize on the opportunities of IoT.
This document discusses how Encanvas can help businesses leverage IoT and big data. Encanvas manages IoT assets and aggregates their performance data with other sources to generate new insights. It gathers, analyzes, and interprets diverse data in new ways to produce value and new business approaches. Encanvas provides a platform to build scalable applications that connect to various data sources, perform extract-transform-load processes, and automate tasks using robotic functionality.
Data science provides businesses with advanced tools and technologies that allow them to automate complicated business processes linked with extracting, analyzing, and presenting raw data.
With so much happening in the technical field, and the data being generated at a rapid speed, it is crucial to know about the latest as well as the upcoming trends in data science.
This document discusses Oracle's Internet of Things platform for connecting machines and devices. It describes how Oracle provides a complete solution to develop and deploy applications across devices and data centers, manage and analyze large volumes of machine-generated data, integrate device data with enterprise applications, protect data through all stages of processing with security and compliance capabilities, and optimize business operations and innovation with Oracle applications and engineered systems.
The document provides an overview of digital marketing and e-commerce trends. It discusses key concepts such as digital marketing, types of e-commerce like business-to-business and business-to-consumer, the growth of mobile commerce, and social media marketing platforms like Facebook and Twitter. Examples of typical marketing campaigns on Facebook and Twitter are also summarized.
This presentation was done for our term paper where we went to two organizations namely Shwapno and The City Bank ltd of Bangladesh and conducted survey on the employees about how they were using their information systems and how much comfortable they were. a brief discussion of information systems is provided here with a discussion on nformation systems in perspective of Bangladesh.
The Internet of Things is an emerging topic of technical, social, and economic significance. Consumer products, durable goods, cars and trucks, industrial and utility components, sensors, and other everyday objects are being combined with Internet connectivity and powerful data analytic capabilities that promise to transform the way we work, live, and play. Projections for the impact of IoT on the Internet and economy are impressive, with some anticipating as many as 100 billion connected IoT devices and a global economic impact of more than $11 trillion by 2025.
This document discusses how businesses can transform by gaining insights from data collected by internet-connected devices and sensors. It explains that the Internet of Things allows physical objects to connect, share data, and interact. While much data from connected devices is unused, businesses can analyze this data using advanced analytics to gain insights, optimize operations, innovate new products and services, and improve customer engagement across all industries. The document outlines IBM's approach to helping businesses capture opportunities from the Internet of Things.
Digital Futures Webinar with Amaze CSO Rick Curtis Jan 2014amazeplc
Amaze's Chief Strategy Officer, Rick Curtis gives his thoughts on what will be hot in digital over the next 24 months and beyond. Rick discussed the following areas:
• The trends that will change the way businesses function and how we live our lives through 2014 and beyond
• What businesses should be thinking about as they compete in the continually evolving technological landscape
• What businesses need to be doing in order to remain competitive in a future being driven by Connectivity, Context and Collaboration.
To view a recording of this webinar, please click here:
https://vimeo.com/amazeplc/review/83694979/18dc31fd96
The document discusses findings from a survey conducted by GE and Accenture on how industrial companies are approaching and investing in Big Data analytics and the Industrial Internet. Key findings include:
1) Executives see Big Data analytics as critical and are investing over 20% of technology budgets in it, expecting investments to increase.
2) Board-level support is the primary driver of Big Data strategies over other executives.
3) Companies feel a sense of urgency to implement Industrial Internet solutions as 84% believe it can shift competitive landscapes in a year and competitors are leveraging it.
4) Companies are moving beyond basic asset monitoring with Big Data to optimize operations and create new revenue through predictive maintenance, efficiency gains
Should I Choose Machine Learning or Big Data?Bernard Marr
Big Data and Machine Learning are two exciting applications of technology that are often mentioned together in the space of the same breath. In reality, there are important distinctions that need to be understood when we are making decisions about our business data strategy.
Top 10 Technology Predictions - Future Outlook for AI and DLTAPPANION
Artificial Intelligence and Distributed Ledger Technology are the current hot topics on the innovation agenda. In our future outlook for 2019 and beyond, we made 10 bold predictions for the upcoming development of these two key technologies.
The Human Side of Artificial IntelligenceBernard Marr
Sometimes it's easy to forget that humanity is the most important element of artificial intelligence (AI). AI is created by humans to solve human problems and to do so by emulating our own abilities to think, learn, and improve. A new Microsoft initiative aimed at broadening the understanding – and assuaging the fear – of AI has been developed with this important fact in mind.
"Data Science is highly resourceful when it comes to an understanding of the public and their decisions for service providers' products and services. The top 8 exciting trends that our world will be able to see in Data Science in the coming year of 2021 are discussed here. "
The New Global AI Arms Race: How Nations Must Compete On Artificial IntelligenceBernard Marr
As governments race to unlock AI’s potential within their countries, who is leading the pack, and who is falling behind? Get the latest research and advice about what it takes to get an advantage in the global AI arms race.
3-part approach to turning IoT data into business powerAbhishek Sood
There will be 44 zettabytes of data produced by IoT alone by 2020, according to IDC. That’s a little more than the cumulative size of 44 trillion feature films.
Data from IoT devices will soon be table stakes in your industry, if it isn’t already. Turning that data into quick and actionable insights is the race for all businesses who are investing in IoT devices.
Learn about a 3-pronged approach that can turn your IoT data into business actions:
Business-wide analytics revolution
Connected relationships with customers
Intelligent innovation based on data
This white paper discusses how organizations can transform big data into business value by connecting various data sources, analyzing data at scale, and taking action. It outlines the challenges of dealing with exponentially growing data in today's digital world. The paper introduces Actian's solutions for enabling an "action-driven enterprise" through its DataCloud Platform for invisible integration and ParAccel Platform for unconstrained analytics. These platforms allow organizations to connect diverse data, analyze it without constraints, and automate actions based on insights gleaned from big data analytics. Use cases demonstrate how companies are leveraging Actian's technology to gain competitive advantages.
The document discusses how the rise of the Internet of Things will require organizations to adapt their corporate structures and executive roles. As IoT connects more devices and generates unprecedented data, executives must work together across functions like operations, technology, information, marketing and human resources. The CEO must recognize opportunities in big data while the CIO manages vast information flows. As privacy and security challenges emerge, the CSO and CLO must collaborate to ensure compliance. Overall, success in the IoT era will depend on innovation through interdependent relationships between C-level executives.
What Are The Latest Trends in Data Science?Bernard Marr
The benefits of a data-driven approach to business are well established but not set in stone. The relentless march of technological progress means the boundaries of what is possible are constantly being redrawn, spawning new behaviors, trends and buzzwords.
From hype to action getting what's needed from big data agwdeodhar
The document discusses the challenges companies face in realizing value from big data analytics. While big data holds potential for competitive advantage, most companies still struggle with managing vast amounts of data from various sources and finding ways to gain useful insights. Early adopters have found success, but full adoption of big data analytics remains limited due to challenges like lack of skills and understanding how insights can impact organizations. The document argues that in order to benefit, companies need solutions that easily manage the entire data workflow and provide insights to business users in a self-service manner.
From Hype to Action-Getting What's Needed from Big Data Agwdeodhar
The document discusses the challenges companies face in realizing value from big data analytics. While big data holds potential for competitive advantage, most companies still struggle with managing vast amounts of data from various sources and finding ways to gain useful insights. Early adopters have found success, but full adoption of big data analytics remains limited due to challenges like lack of skills and siloed efforts. For big data analytics to become mainstream, companies need help managing the entire data pipeline workflow and delivering insights to business users effectively.
The document discusses the top 10 technology trends driving the 4th Industrial Revolution according to Bernard Marr. The trends are: 1) artificial intelligence and machine learning, 2) the internet of things, 3) big data, 4) blockchains, 5) cloud and edge computing, 6) robots and cobots, 7) autonomous vehicles, 8) the 5G network, 9) genomics and gene editing, and 10) quantum computing. Marr believes these technologies will transform our lives and the world in the next decade.
This document discusses emerging technology trends and provides an overview of several key trends: smart machines, artificial intelligence, 3D printing, augmented reality, predictive analytics, the internet of things, big data, and wearables. The author's goal is to help the audience understand these rapidly changing technologies and how they will impact how people interact with technology. Each trend is defined and examples are given to illustrate real-world applications and leaders in each field.
This document discusses how cognitive computing can help realize the full potential of the Internet of Things (IoT). It notes that while early IoT applications are providing value, the vast majority of data generated by IoT devices is currently unused. Cognitive systems that can learn from large amounts of structured and unstructured data have the potential to extract much more insights from IoT data and enable more advanced IoT applications. The document outlines some key foundations for a successful IoT strategy and argues that cognitive systems like IBM's Watson platform can help address the data challenges of IoT by facilitating deeper human engagement, continuous learning, predictive capabilities, knowledge sharing and optimization of complex systems.
In this Whitepaper Dennis Curry explores the impact of the Internet of Things on the corporate environment, highlighting the importance of building intuitive associations in disparate and highly complex data.
Harbor Research recently completed a review of a new
cloud-based platform that takes a refreshingly new
approach to machine data analytics. Glassbeam jumps
ahead of the current market’s noise and confusion about
Big Data by viewing critical machine data analytics from a
business and operational perspective that can be addressed
by a single, scalable solution. In so doing, Glassbeam is
re-defining how value is created from machine data.
This whitepaper provides an overview of artificial intelligence (AI) and its commercialization. It discusses the history and development of AI from early pattern recognition (AI 1.0) to today's deep learning (AI 2.0) to the emerging contextual reasoning (AI 3.0). Key points include how transfer learning and increased computing power are driving new AI applications and how AI is being applied commercially in healthcare, manufacturing, logistics, and other industries. The document also addresses the global demand for AI talent and the challenges of developing reliable AI systems that can operate under changing conditions.
Top 10 Emerging Technology in 2022.docxAdvance Tech
It's hard to believe that we're already nearing the end of 2020 - and with a new year comes new opportunities for businesses to adopt emerging technologies. But with so many options on the market, it can be tough to know which ones are worth investing in. In this article, we'll count down the top 10 emerging technologies that are expected to make a big impact in 2022!
here is always new and emerging technology. Some of it is mind-blowing and has the potential to change the world. Here are the top 10 emerging technologies in 2022
It's hard to predict the future, but that doesn't mean we don't try. In this article, we'll take a look at 10 different technologies that are emerging and could potentially have a big impact in the next few years. From Blockchain to quantum computing, these are the technologies you should keep an eye on!
https://advancetech.info/emerging-technologies/
Top 6 New Technology Trends For 2022.docxSameerShaik43
This document outlines the top 6 new technology trends for 2022, which are robotic process automation (RPA), artificial intelligence and machine learning, quantum computing, edge computing, internet of things (IoT), and blockchain. It provides a brief description of each trend, noting that RPA can automate many business processes, AI and machine learning are evolving and creating new jobs, quantum computing is faster than normal computers, edge computing processes time-sensitive data with limited connectivity, IoT connects many devices to share data, and blockchain offers secure transactions without third parties. Adopting the right new technologies can help businesses increase profits, improve customer experience, and reduce costs.
PTC Product Lifecycle Stories eMagazine - Spring 2014PTC
The document discusses how the Internet of Things (IoT) is transforming manufacturing through connecting billions of devices that can be remotely monitored and controlled. This allows manufacturers to more efficiently create, operate, and service products. It describes how dropping sensor costs are enabling more connectivity and data collection from products in use. This data can optimize operations, predict failures to schedule maintenance, and help manufacturers learn how customers use their products to develop new versions. While data privacy and security are concerns, the opportunities of IoT for manufacturers include reducing costs, improving products, and creating new after-sale revenue through services.
We are citizens of a data-driven century in the early stages of a digital industrial revolution. Abundant data by itself solves nothing. The document discusses challenges that companies face with managing large amounts of industrial data from connected machines, including data being scattered across different systems, or "islands of disparate data", which makes it difficult to extract insights. It also outlines opportunities for companies to gain competitive advantages by better utilizing industrial data through new management systems and analytics.
Future Trends of AI and Analytics in the CloudBernard Marr
Artificial intelligence (AI) has the potential to be the most powerful and transformative technology the business world has ever seen, helping us make smarter decisions, automate tasks and fully realize the value of the data businesses are generating at an ever-growing rate.
Artificial intelligence and Internet of Things.pptxSriLakshmi643165
The document discusses artificial intelligence (AI) and the Internet of Things (IoT). It defines AI as using machines to simulate human intelligence through learning, reasoning and self-correction. IoT is defined as the network of physical devices connected through software and sensors to exchange data. The document outlines key applications of AI in healthcare, retail, education and more. It also discusses applications of IoT in healthcare, traffic monitoring, agriculture and fleet management. Finally, it discusses the future integration of AI and IoT, noting their potential to optimize systems, provide personalized recommendations and enable predictive maintenance through analysis of data collected by IoT devices.
How Artificial Intelligence Will Kickstart the Internet of Thnigs Ahmed Banafa
The possibilities that IoT brings to the table are endless.
IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT.
This document provides an introduction to WSJ Pro Artificial Intelligence, a new offering from The Wall Street Journal that aims to help businesses understand and draw value from the rise of artificial intelligence. The summary discusses the impact of AI on businesses, how WSJ Pro AI will assess the effects of AI on different levels and issues of companies, and provides examples of the types of journalism that will be included.
James Jones SpaceX talks about New Emerging Technologies.pdfJames Roland Jones
James Jones SpaceX says Technology solutions built around artificial intelligence (AI) and 5G provide the greatest immediate opportunities for technology companies to drive new business and revenues. As emerging technologies disrupt businesses, Industry 4.0 in manufacturing and digitalization across all other industries are speeding up deployments of IoT, blockchain, AI, machine learning, cognitive intelligence, deep learning, etc. As these emerging technologies continue to evolve, they are enabling intelligent automation of daily processes, thereby unlocking infinite business opportunities.
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TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
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the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
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for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
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International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
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1. University of Burgundy – Dijon
Master’s Degree DB & AI
November 2018
Artificial Intelligence and Big Data
a revolutionary convergence
“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all
industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the
possibilities offered by the alliance between Big Data and AI. “
Hatim El-Qaddoury
hatim-elqaddoury@outlook.fr
2. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
2
Outline
The revolution of AI and Big Data: what is artificial intelligence?.....................................................................3
Artificial Intelligence definition of a technology that inspires.............................................................................3
Big Data and AI are the next digital disruptions..................................................................................................3
What are the challenges for Big Data and Artificial Intelligence?......................................................................4
Can Big Data solve the problems and dangers of artificial intelligence? ...........................................................5
Example of Artificial Intelligence feeding on Big Data .......................................................................................6
The tools of artificial intelligence for the ABI.................................................................................................6
Data Crawlers...................................................................................................................................................6
Natural language processing ............................................................................................................................6
Machine Learning ............................................................................................................................................7
Google, Huawei, Apple, IBM ... who are the giants of artificial intelligence and Big Data................................7
Google and DeepMind .....................................................................................................................................7
Amazon ............................................................................................................................................................7
Apple................................................................................................................................................................8
Facebook ..........................................................................................................................................................8
Microsoft..........................................................................................................................................................8
Artificial Intelligence example: 5 big data trends that will lead the evolution of AI in 2019 ..............................8
Big Data self-service tools available on the web .............................................................................................9
Analytical technologies are struggling to adapt ...............................................................................................9
Data cleaning becomes an industry..................................................................................................................9
The democratization of data.............................................................................................................................9
3. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
3
• The revolution of AI and Big Data: what is artificial intelligence?
We are at the dawn of a technological revolution of greater magnitude than the internet and mobile
communication technologies. In 1965, Gordon Moore, co-founder of Intel, theorized that computing power
would be able to double every 18 to 24 months. For the next 50 years, his theory proved to be accurate. The
High-Tech sectors of robotics or biotechnology have made incredible progress.
Today, however, technologies like AI and Big Data are evolving even faster. The respective exponential
growths of these two technologies are about to come together, allowing each to grow even faster. Artificial
intelligence is no longer simply a film or a book. Elon Musk, Stephen Hawking and even Cédric Villani are
some of the personalities to discuss the consequences on a large scale.
• Artificial Intelligence definition of a technology that inspires
In 1990, a group of scientists began to decode the human genome. A process that would take them no
less than 13 years, and would cost them $ 2.7 billion. This decryption would not have been possible without the
help of immense computer power and custom software. Thanks to the fall in computer prices, the researchers
were then able to undertake editing the genome using the CRISPR technique. At present, analytical technologies
of big data will make it possible to develop medical treatments adapted to each one according to his genetic
code.
Autonomous cars have always held a special place in science fiction. Today, reality is catching up with
the imagination. In 2009, many luxury brands incorporated assisted navigation systems and adaptive data-driven
channel change software. More recently, Tesla has used Big Data and AI to create autopilot functionality.
For their part, Nvidia and Alphabet use artificial intelligence to make real-time detailed maps used by
their test vehicles to visualize the world. They are based on deep learning which itself is based on a network of
neurons. The commerce industry is also evolving. Product development and marketing are now driven by AI
and Big Data. All these fascinating innovations have been made possible by the encounter between computing
power, big data and artificial intelligence.
• Big Data and AI are the next digital disruptions
The big data and artificial intelligence technologies are both inextricably linked, so that a Big Data
Intelligence can speak. AI has become ubiquitous in companies in all industries in which decision-making is
transformed by intelligent machines. The need for smarter decisions and big data management are the criteria
that drive this trend.
4. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
4
The convergence between Big Data and AI seems inevitable as the automation of smart decision-making
becomes the next evolution of Big Data. Rising agility, smarter business processes and higher productivity are
the most likely benefits of this convergence.
The evolution of data management did not go smoothly. Much of the data is now stored on a computer,
but there is still a lot of information on paper, despite the possibility of scanning paper information and storing
it on disks or in databases.
You just have to go to a hospital, an administration, a doctor's office or any business to realize that a lot
of information about customers, vendors, or products is still stored on paper. However, it is impossible to store
terabytes of data produced by streaming video, text and images on paper.
The mere fact of collecting or having access to large sets of data is not enough to produce a result. Most
of us are not sufficiently prepared for the knowledge extraction and the demand for rapid decision-making
required by customers and markets to maintain a competitive advantage.
Today, the use of machine learning, expert systems and analytical technologies in combination with Big
Data is presented as the natural evolution of these two disciplines. Convergence is inevitable.
The Internet of Things also represents a convergence between Big Data and Artificial
Intelligence. Without a digitized human brain intelligent enough to allow humans to use an IoT network that
can process, distribute and collect Big Data, it will not be possible to set up such a network.
Even the sensors, chips, network nodes and software that make IoT networks work on the cloud will be
related to artificial intelligence. This phenomenon is already in place in the field of Machine to Machine
communications.
The capture data to identify trends or patterns in the behavior of customers or employees can be very
helpful. However, the extraction of meaning, and its automation, to discover optimal methods of improving
productivity or problem solving could be even more useful.
Artificial intelligence will be used to extract meaning, determine better results, and enable faster
decision-making from massive Big Data sources. In a world where Big Data is ubiquitous, the extraction of
meaning, the monetization of data will be led by artificial intelligence for the future of business and the
development of the planet. The convergence of Big Data and AI could help overcome challenges such as
unemployment, the environment, the economy, security or health.
Automation of decision making is slowly becoming the norm. Many problems concerning the ethics of
artificial intelligence have yet to be solved. Systems capable of learning autonomously, responsible for
determining which Big Data should be identified and used, will require human management, at least initially.
In the fields of healthcare, law, banking, advertising, fair trade, security or finance, big data alone is not
enough. It is necessary to use artificial intelligence in addition.
It is therefore important not to make the mistake of perceiving these two technologies as two separate
tendencies. Your business might miss an opportunity. This convergence will have a direct impact on your
employees, your customers, your services and your market and must be considered.
• What are the challenges for Big Data and Artificial Intelligence?
5. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
5
For the moment, AI is not regulated specifically. Many people express security concerns. This problem
needs to be resolved quickly. Any information can be easily stolen by hackers. Highly sophisticated models
make us vulnerable to many threats.
Moreover, many worries about the control around this technology. The lack of laws to govern the sales
and purchase of artificial intelligence software. If these programs are intended to control traffic, health systems,
or the stock market, it is necessary to put in place governance laws.
There is no doubt that autonomous decision-making is the future. However, again, many fears are
emerging about the authenticity and ethics of artificial intelligence and Big Data. The accumulation of data on
cloud servers and its accessibility to fraudsters can be fatal for businesses.
All these challenges are daunting. They give rise to suspicion around this convergence between
Artificial Intelligence and Big Data. It is important to remember that technologies are only disruptive when we
are poorly prepared.
• Can Big Data solve the problems and dangers of artificial intelligence?
Over the last four years, agreements between large companies and startups dedicated to artificial
intelligence have increased significantly. This number increased from 160 in 2012 to 658 in 2016. Companies
use AI for a wide variety of uses, ranging from autonomous car development to remote emotion detection.
Apart from these uses, artificial intelligence can be even more useful for businesses through what is called
Account-Based Intelligence.
Account-Based Intelligence is the latest iteration of the dream of sales and one-to-one marketing. Today,
we are closer than ever to achieving this utopia.
6. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
6
• Example of Artificial Intelligence feeding on Big Data
First, we are generating more data today than ever before. Every second, humanity produces 6000
tweets, 40,000 Google searches, and 2 million emails. By 2019, global web traffic will surpass 2 zettabytes per
year.
This huge amount of data is the first step towards Account-Based Intelligence, because the ABI requires
granular information about each target company. However, it also raises a new problem. Companies must find
how to turn this data into exploitable insights.
Indeed, this task is impossible to accomplish using traditional marketing tools or simple Google
searches. The web is too massive is disorganized to achieve it as well. Many companies spend millions of dollars
to mix data sources and solution points, which ultimately results in only a very low conversion rate. For good
reason, this method usually results in sending the wrong message to the wrong people at the wrong time.
• The tools of artificial intelligence for the ABI
Until recently, computers struggled to interpret unstructured data like Facebook content and YouTube
videos. However, with recent advances in cognitive computing and processing power, things are changing.
However, this change can benefit businesses for their sales and marketing. Indeed, information on
business leaders, the decisions they make, their attitude and demographics are not stored properly within small
databases. They are scattered in social media publications, browsing history and geolocation data. Today, new
tools allow startup leaders to make sense of this data.
• Data Crawlers
The Data Web Crawlers undermine autonomously in search of unstructured data. They examine entities,
establish relationships, and create customer profiles. With an estimated 70 percent increase in data per year, it
is critical that these programs continually scan the web for the most relevant information.
Startups can use them to deploy the ABI. For example, to find new customers, browsing the web can
reveal a niche of customers whose demographics match those of the best current customers.
In 2015, Microsoft acquired Mantanani for this purpose. By using crawlers, the startup can explore a large
amount of non-relational data. She then recovers insights from different sources faster and more accurately than
humans.
• Natural language processing
The natural language processing can examine the interactions between computers and humans to extract
meaning from conversations. By spotting some words or phrases, this technology helps to analyze feelings about
the brand. It also predicts which audiences will be more receptive to the company's message. This is essential
in order to communicate the right message to the right people, which is the primary criterion of the ABI.
If the company wants to know what people are saying about its products on social networks, natural
language processing can explore social media publications, link them with certain consumer groups, and find
out what's important the most for each group. This system can be used to respond to consumer criticism and
positive reviews, to solve problems, and to improve a product.
If you want to try this technology for yourself, be aware that the IV.AI startup allows anyone to try out
their natural language processing platform. Type any phase to know the emotion that corresponds to it.
7. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
7
• Machine Learning
The Machine Learning allows computers to learn and act without being programmed
explicitly. This technology looks for patterns within the data to drive the actions of an Artificial Intelligence
program, considering the context. The true ABI requires dynamic templates, and the Learning machine
automatically adjusts them as new data emerges.
Without even knowing it, new companies are already taking advantage of Machine
Learning. Facebook uses this technology to personalize the news feed based on clicks and likes. Other
companies use this technology to predict customer loyalty or purchasing behavior, predict product performance,
or anticipate risks.
Google Now is probably the most advanced Machine Learning app yet. She learns user habits, mimics
their conversation style, and provides them with smart recommendations. For example, if the user needs to go
to the airport for a flight that will take place in 30 minutes, Google Now can analyze the traffic delays and
schedule an Uber that will take him there on time.
Artificial intelligence is strong, it without a doubt a great technology. It can find data inaccessible to
humans, and distill meaning with great precision. Combined with the ABI, it can also guide the company to its
next best customers. This technology will be the biggest change of the century in the field of business, and the
revolution is just beginning.
• Who are the biggest players of artificial intelligence and Big Data?
Artificial intelligence is a technology in full swing, and many startups around the world are looking to
seize the opportunities it offers. However, as Prometheus seizes fire, the tech giants are determined to keep this
precious resource for them.
Thus, according to CB Insights, 115 of the 120 AI firms that left the market in 2017 did so through an
acquisition. The Silicon Valley behemoths are willing to spend billions to buy the most promising startups, and
AI is now the new high-tech war front.
• Google and DeepMind
Google is clearly determined to dominate the nascent AI industry. Over the past four years, Mountain View has
acquired 12 startups in this area. However, Big G is not currently seeking to market a product. His goal seems
to be to improve his various services through AI. In parallel, it is also developing its TensorFlow development
platform and its Tensor AI chip.
In 2014, Google bought DeepMind for $ 500 million. This London company also counts among the leaders of
AI. It has a predominant role in the field of artificial intelligence research and Machine Learning. In particular,
she created an AI capable of detecting eye disorders with the same precision as a human expert, and
developed an AI assistant for doctors and nurses.
https://youtu.be/rsN690cfWsM
• Amazon
The e-commerce giant is also involved in the field of AI. It offers products and services for individuals and
businesses. Thus, Amazon Echo brings the artificial intelligence in the households of the individuals via the
voice assistant Alexa. Similarly, the AWS Cloud offers three leading AI services for professionals: Lex is a
8. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
8
version of Alexa for business, Polly transforms text into speech, and Recognition is an image recognition
service. https://youtu.be/2DtyjC0UxTw
• Apple
Between 2016 and 2018, Apple acquired four artificial intelligence startups. One of them has enabled the
creation of Faced, the revolutionary facial recognition system of the iPhone X. Similarly, the Apple has been
devoting for several years its virtual assistant Siri. Recently, Apple has also taken over the former director of
Google's AI. There is no doubt that the Cupertino company has many surprises for the future ...
https://youtu.be/K72wjPomTe4
• Facebook
The Facebook AI Research (FAIR) brings together four artificial intelligence labs scattered around the
world. Their objective? Use AI to understand how humans communicate. Recently, the firm acquired four
startups of artificial intelligence. The most recent is Ozlo, which seeks to create a better virtual assistant for
Messenger. https://youtu.be/-CRJLam3BNc
• Microsoft
The creator of Windows also has many AI projects, both for the general public and for businesses. For
consumers, the Redmond company develops the virtual assistant Cortana for Windows or the chatbot Zo. For
professionals, Microsoft offers various AI services on its Azure cloud: chatbots, machine learning, Cognitive
Computing ... https://youtu.be/XopvSz4GpEc
• AI example: 5 big data trends that will lead the evolution of AI in 2019
The rise of Artificial Intelligence and Machine Learning is highly dependent on Big Data. The data
makes it possible to develop predictive models. The more data that are numerous, and representative of the
concepts to be learned, the more Machine Learning AI applications are completed.
In 2017, we should see more experts in this area, but demand should remain above supply. Machine
Learning promotes the adoption of Big Data solutions, just like the cloud that facilitates their deployment.
9. University of Burgundy
Master’s Degree DB & AI
Artificial Intelligence and Big Data
a revolutionary convergence
9
• Big Data self-service tools available on the web
With advances in data processing applications, there are many free online Big Data platforms
available. These cloud platforms make it easy to organize and synthesize data, even for beginners.
It is enough for the user to specify the amount of storage and computing power it needs, and the
databases appear in the cloud in minutes. No need to configure racks, networks or servers.
For Michael Cigarette, Director of Analytical Infrastructure at Ford Motor Company, this trend is
expected to continue in 2017. Big Data's cloud implementations are becoming increasingly popular as they
reduce the cost of accessing these technologies. For many, developing a Big Data stack is not cost effective, and
works best when most data can be hosted on an individual instance.
• Analytical technologies are struggling to adapt
Even with state-of-the-art tools and data warehouses such as Hadoop and Spark, data analytics remain
complex. Companies struggle to transfer their data from operational systems to analytical systems. This
difficulty directly affects productivity.
The data available is no longer numerous, and the algorithms are improving, allowing more automation
and better predictions. In fact, analytical technologies are struggling to adapt.
• Data cleaning becomes an industry
To transfer data to Machine Learning systems, it is necessary to clean them first. Cleaning up data means
looking for errors in the format or duplications within the database. The quality of Machine Learning systems
depends on the data on which they are based. The secret is to turn raw data into actionable data. For example,
knowing that someone has visited an online shoe store is helpful, but knowing when he or she visited is an
invaluable piece of information.
• The democratization of data
The data director of the Toyota Research Institute believes that data does not reside in data lakes but in
silos in which their mission is clear. Server-less and micro-service architectures make it much easier for owners
of these silos to access, analyze, and manage their data without having to rack up servers, configure virtual
machines, or even to the payment by the hour. Data owners can therefore focus on data enforcement and pay
for what they use by the minute.
Artificial intelligence is a sector with great potential to transform the fields of science, medicine and
technology. We can only advise companies to be ready to embrace this new technology.
The Big Data and AI are emerging technologies, and it is impossible to predict their effect on the long
term. It would be absurd, however, to ignore these technical advances. These two technologies are likely to
converge in the very near future.