Category Archives: Artificial Intelligence
Artificial Intelligence (AI) and Accounting
Smacc, a German-based software firm, uses artificial intelligence to help freelancers, small companies, and medium-sized enterprises automate their accounting systems and financial reporting. They received $3.5M in Series A financing from a variety of high-profile venture capitalists and angel investors, and the founders developed the concept after experiencing difficulties with accounting in the initial stages of their own startup company.
Smacc clients transmit their receipts, which are then converted into machine-readable form. The receipts are allocated to the proper account after encryption. Over time, the system teaches itself to improve its functions: sales, expenses, invoice management, and liquidity profiles.
The software uses more than 60 data points to review receipts and invoices. It checks whether the math is accurate and verifies whether the issuer is correct with details like Value Added Tax (VAT) identification numbers. Whenthe software has learned how to handle each supplier, tasks are subsequently handled automatically. Its artificial intelligence allows it to self-learn and constantly improve its ability to sort and allocate information.
Customers can check their billing and expense data in real-time online, and no longer have to input data or wait around until the end of the month to see where their finances stand. Several companies, such as QuickBooks, offer cloud-based accounting software, but Smacc is among the first to leverage artificial intelligence to improve the softwares ability to automate tasks.
The world of accounting is just the latest in a series of industries being affected by the rapid increase in the use of artificial intelligence. Bill Gates even referred tothe rise of artificial intelligence as computer science's "Holy Grail." After many failed efforts in the past, the accuracy and speed of today's artificial intelligence are much improved.
You cannot go a day without someone on your Facebook feed sharing an article about artificial intelligence and how it will take your job in the next few years, butthese concerns are not new. The same fears were at the forefront of people's minds as factories spread throughout Britain 200 years ago.
Robots are already used throughout our homes, workplaces and entertainment centers, and over the next 10 years, Forrester Research estimates thatAI will take over up to 16% of jobs in the United States. Google believes that robots will achieve human intelligence levels by 2029, and Gartner estimates that 33% of all occupations will be performed by smart robots by 2025. FOW predicts five areas will feel the most impact: healthcare, manufacturing, transportation, customer service, and finance.
With all that said, accountants more than likely do not have to worry about artificial intelligence for a long time. Smacc is developing interesting AI applications to help further automate and streamline bookkeeping tasks, and cloud-based accounting software packages such as QuickBooks say they are already 75% automated. That said, Professional accountants do much more than keep track of receipts and provide basic reports. They act as consultants who advise on tax planning, discuss operations, review client goals, and more. The rapid pace of change in client industries and the expansion of complicated regulations means that human controller services will be necessary to ensure that compliance requirements are met and financial controls are sound.
This is especially true for companies that operate in multiple countries. It is difficult enough to deal with taxes in your home country, but making sense of the tax code and business regulations in a number of foreign countries is daunting. Are AI robots ready to deal with the tangled web of regulations associated with the European Union or the compliance requirements of the Organization for Economic Cooperation and Development (OECD)? No artificial intelligence algorithms that can sort out these complex interactions currently exists.
Machine learning can be trained to handle an amazing variety of tasks if you give it a wide enough variety of examples to draw from. Data scientists are not exactly sure how this happens. The math is so complex, it's difficult to re-engineer it to see how the system learns, which makes diagnosing problems difficult.
AI can do amazing things, but it's not so good at themany things humans do naturally. We make a lot of decisions based on context. Professional controller services understand the rules and regulations their clients must adhere to, and they're able to present options and recommendations in a manner the client can understand.
Present-day machine learning systemsdon't handle thistype of context well. Futurists have proclaimed the benefits of AI for decades now, describing amazing worlds where robots make your everyday life one of ease and relaxation. That future may be here faster than you think, but for now, outsourced accounting services have an advantage the most advanced algorithms cannot duplicatethe human touch.
Council of Europe and Artificial Intelligence
Artificial intelligence and human rights
Artificial Intelligence raises important and urgent issues. AI is already with us changing the information that we receive, the choices that we make, and the ways in which our societies function. In the coming years AI will play an even greater role in the way that governments and public institutions operate, and the way in which citizens interact and participate in the democratic process.
It is clear that AI presents both benefits and risks. We need to ensure that AI promotes and protects our standards. I look forward to the outcome of the work of the Ad hoc Committee on Artificial Intelligence (CAHAI), mandated by the Committee of Ministers to examine the feasibility and potential elements on the basis of broad multi-stakeholder consultations, of a legal framework for the development, design and application of artificial intelligence, based on the Council of Europes standards on human rights, democracy and the rule of law. [Full statement]
Marija Pejinovi BuriSecretary General of the Council of Europe
Artificial intelligence (AI) will have an impact on our societies that we hardly imagine. Algorithms are already said to be able to identify the best candidates for a job, assist doctors to establish medical diagnoses or help lawyers before the courts. All this is not entirely new, since already in the 1980s, expert systems assisted humans with a high level of expertise. What is new today is that computers are increasingly able to perform extremely complex tasks independently, but their designers sometimes no longer understand how, what has happened in the "black box" of deep learning.
Therefore, we clearly need regulation to leave essential decision-making to humans and not to mathematical models, whose adequacy and biases are not controlled. [Full statement]
Jan KleijssenDirector, Information Society - Action against Crime
@JKleijssen
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Council of Europe and Artificial Intelligence
Artificial Intelligence Certification | AI Certification …
The ARTIBA certification mandates you to meet certain educational and work experience requirements to become eligible. The requirements for the certification are unique, so before proceeding, check if you satisfy the minimum eligibility requirements for the AIE certification.
It is recommended that you keep this information handy as it will be required while filling up your online application.
Once you have satisfied the AIE eligibility requirements, it is time to apply.Start the application process by creating your myARTIBA account. Complete the AIEonline application form. Once you have submitted the online application and madea successful payment of the fee, you will immediately receive an acknowledgementresponse email with instructions for the next step in the assessment process.
An internal check is conducted to validate your credentials against the prescribedcandidacy prerequisites for earning the AIE certification.
Post your payment confirmation, you will receive your unit of the AIE LearningDeck containing reading and learning material at the address you have registeredwith us. Normal delivery timeframe is 3-4 weeks.
Your exam window opens exactly 45 days after you have successfully Registered inAIE and paid your fee. You can prepare for and take your AIE exams within thenext 135 days. The books includedin the AIE Learning Deck should help you brace up nicely for your AIE Certificationexam. Please read more about AIE exams in the dedicated section on the subject.
If you qualify the AIE certification exam and meet other conditions laid down bythe ARTIBA Professional Certifications Board (APCB), you are recommended for theaward of AIE Credential and the AIE Digital Badge is immediately issued to you.You can expect the shipment of the physical credential pack to reach you within3-4 weeks from the date of the credential- award.
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Artificial Intelligence Certification | AI Certification ...
Artificial Intelligence in Fintech – Global Market Growth, Trends and Forecasts to 2025 – Assessment of the Impact of COVID-19 on the Industry -…
DUBLIN--(BUSINESS WIRE)--The "AI in Fintech Market - Growth, Trends, Forecasts (2020-2025)" report has been added to ResearchAndMarkets.com's offering.
The global AI in Fintech market was estimated at USD 6.67 billion in 2019 and is expected to reach USD 22.6 billion by 2025. The market is also expected to witness a CAGR of 23.37% over the forecast period (2020-2025).
Artificial Intelligence improves results by applying methods derived from the aspects of human intelligence but beyond human scale. The computational arms race since the past few years has revolutionized the fintech companies. Further, data and the near-endless amounts of information are transforming AI to unprecedented levels where smart contracts will merely continue the market trend.
Key Highlights
Major Market Trends
Quantitative and Asset Management to Witness Significant Growth
North America Accounts for the Significant Market Share
Competitive Landscape
AI in Fintech market is moving towards fragmented owing to the presence of many global players in the market. Further various acquisitions and collaboration of large companies are expected to take place shortly, which focuses on innovation. Some of the major players in the market are IBM Corporation, Intel Corporation, Microsoft Corporation, among others.
Some recent developments in the market are:
Key Topics Covered
1 INTRODUCTION
1.1 Study Deliverables
1.2 Scope of the Study
1.3 Study Assumptions
2 RESEARCH METHODOLOGY
3 EXECUTIVE SUMMARY
4 MARKET DYNAMICS
4.1 Market Overview
4.2 Industry Attractiveness - Porter's Five Force Analysis
4.2.1 Bargaining Power of Suppliers
4.2.2 Bargaining Power of Buyers/Consumers
4.2.3 Threat of New Entrants
4.2.4 Threat of Substitute Products
4.2.5 Intensity of Competitive Rivalry
4.3 Emerging Use-cases for AI in Financial Technology
4.4 Technology Snapshot
4.5 Introduction to Market Dynamics
4.6 Market Drivers
4.6.1 Increasing Demand for Process Automation Among Financial Organizations
4.6.2 Increasing Availability of Data Sources
4.7 Market Restraints
4.7.1 Need for Skilled Workforce
4.8 Assessment of Impact of COVID-19 on the Industry
5 MARKET SEGMENTATION
5.1 Offering
5.1.1 Solutions
5.1.2 Services
5.2 Deployment
5.2.1 Cloud
5.2.2 On-premise
5.3 Application
5.3.1 Chatbots
5.3.2 Credit Scoring
5.3.3 Quantitative and Asset Management
5.3.4 Fraud Detection
5.3.5 Other Applications
5.4 Geography
5.4.1 North America
5.4.2 Europe
5.4.3 Asia-Pacific
5.4.4 Rest of the World
6 COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 IBM Corporation
6.1.2 Intel Corporation
6.1.3 ComplyAdvantage.com
6.1.4 Narrative Science
6.1.5 Amazon Web Services Inc.
6.1.6 IPsoft Inc.
6.1.7 Next IT Corporation
6.1.8 Microsoft Corporation
6.1.9 Onfido
6.1.10 Ripple Labs Inc.
6.1.11 Active.ai
6.1.12 TIBCO Software (Alpine Data Labs)
6.1.13 Trifacta Software Inc.
6.1.14 Data Minr Inc.
6.1.15 Zeitgold GmbH
7 INVESTMENT ANALYSIS
8 MARKET OPPORTUNITIES AND FUTURE TRENDS
For more information about this report visit https://www.researchandmarkets.com/r/y1fj00
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Artificial Intelligence in Fintech - Global Market Growth, Trends and Forecasts to 2025 - Assessment of the Impact of COVID-19 on the Industry -...
Bringing the predictive power of artificial intelligence to health care – MIT News
An important aspect of treating patients with conditions like diabetes and heart disease is helping them stay healthy outside of the hospital before they to return to the doctors office with further complications.
But reaching the most vulnerable patients at the right time often has more to do with probabilities than clinical assessments. Artificial intelligence (AI) has the potential to help clinicians tackle these types of problems, by analyzing large datasets to identify the patients that would benefit most from preventative measures. However, leveraging AI has often required health care organizations to hire their own data scientists or settle for one-size-fits-all solutions that arent optimized for their patients.
Now the startup ClosedLoop.ai is helping health care organizations tap into the power of AI with a flexible analytics solution that lets hospitals quickly plug their data into machine learning models and get actionable results.
The platform is being used to help hospitals determine which patients are most likely to miss appointments, acquire infections like sepsis, benefit from periodic check ups, and more. Health insurers, in turn, are using ClosedLoop to make population-level predictions around things like patient readmissions and the onset or progression of chronic diseases.
We built a health care data science platform that can take in whatever data an organization has, quickly build models that are specific to [their patients], and deploy those models, says ClosedLoop co-founder and Chief Technology Officer Dave DeCaprio 94. Being able to take somebodys data the way it lives in their system and convert that into a model that can be readily used is still a problem that requires a lot of [health care] domain knowledge, and thats a lot of what we bring to the table.
In light of the Covid-19 pandemic, ClosedLoop has also created a model that helps organizations identify the most vulnerable people in their region and prepare for patient surges. The open source tool, called the C-19 Index, has been used to connect high-risk patients with local resources and helped health care systems create risk scores for tens of millions of people overall.
The index is just the latest way that ClosedLoop is accelerating the health care industrys adoption of AI to improve patient health, a goal DeCaprio has worked toward for the better part of his career.
Designing a strategy
After working as a software engineer for several private companies through the internet boom of the early 2000s, DeCaprio was looking to make a career change when he came across a project focused on genome annotation at the Broad Institute of MIT and Harvard.
The project was DeCaprios first professional exposure to the power of artificial intelligence. It blossomed into a six year stint at the Broad, after which he continued exploring the intersection of big data and health care.
After a year in health care, I realized it was going to be really hard to do anything else, DeCaprio says. Im not going to be able to get excited about selling ads on the internet or anything like that. Once you start dealing with human health, that other stuff just feels insignificant.
In the course of his work, DeCaprio began noticing problems with the ways machine learning and other statistical techniques were making their way into health care, notably in the fact that predictive models were being applied without regard for hospitals patient populations.
Someone would say, I know how to predict diabetes or I know how to predict readmissions, and theyd sell a model, DeCaprio says. I knew that wasnt going to work, because the reason readmissions happen in a low-income population of New York City is very different from the reason readmissions happen in a retirement community in Florida. The important thing wasnt to build one magic model but to build a system that can quickly take somebodys data and train a model thats specific for their problems.
With that approach in mind, DeCaprio joined forces with former co-worker and serial entrepreneur Andrew Eye, and started ClosedLoop in 2017. The startups first project involved creating models that predicted patient health outcomes for the Medical Home Network (MHN), a not-for-profit hospital collaboration focused on improving care for Medicaid recipients in Chicago.
As the founders created their modeling platform, they had to address many of the most common obstacles that have slowed health cares adoption of AI solutions.
Often the first problems startups run into is making their algorithms work with each health care systems data. Hospitals vary in the type of data they collect on patients and the way they store that information in their system. Hospitals even store the same types of data in vastly different ways.
DeCaprio credits his teams knowledge of the health care space with helping them craft a solution that allows customers to upload raw data sets into ClosedLoops platform and create things like patient risk scores with a few clicks.
Another limitation of AI in health care has been the difficulty of understanding how models get to results. With ClosedLoops models, users can see the biggest factors contributing to each prediction, giving them more confidence in each output.
Overall, to become ingrained in customers operations, the founders knew their analytics platform needed to give simple, actionable insights. That has translated into a system that generates lists, risk scores, and rankings that care managers can use when deciding which interventions are most urgent for which patients.
When someone walks into the hospital, its already too late [to avoid costly treatments] in many cases, DeCaprio says. Most of your best opportunities to lower the cost of care come by keeping them out of the hospital in the first place.
Customers like health insurers also use ClosedLoops platform to predict broader trends in disease risk, emergency room over-utilization, and fraud.
Stepping up for Covid-19
In March, ClosedLoop began exploring ways its platform could help hospitals prepare for and respond to Covid-19. The efforts culminated in a company hackathon over the weekend of March 16. By Monday, ClosedLoop had an open source model on GitHub that assigned Covid-19 risk scores to Medicare patients. By that Friday, it had been used to make predictions on more than 2 million patients.
Today, the model works with all patients, not just those on Medicare, and it has been used to assess the vulnerability of communities around the country. Care organizations have used the model to project patient surges and help individuals at the highest risk understand what they can do to prevent infection.
Some of it is just reaching out to people who are socially isolated to see if theres something they can do, DeCaprio says. Someone who is 85 years old and shut in may not know theres a community based organization that will deliver them groceries.
For DeCaprio, bringing the predictive power of AI to health care has been a rewarding, if humbling, experience.
The magnitude of the problems are so large that no matter what impact you have, you dont feel like youve moved the needle enough, he says. At the same time, every time an organization says, This is the primary tool our care managers have been using to figure out who to reach out to, it feels great.
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Bringing the predictive power of artificial intelligence to health care - MIT News
A.I. Artificial Intelligence shows us a future where we neglect to dream – The Verge
The Verge is a place where you can consider the future. So are movies. In Yesterdays Future, we revisit a movie about the future and consider the things it tells us about today, tomorrow, and yesterday.
The movie: A.I. Artificial Intelligence
The future: A.I. begins with a brief summary of the sorry state of the world: climate change has melted the polar ice caps, wiping out coastal cities and severely reducing the human population. With regulations in place for reproduction on a resource-starved planet, corporations developed Mecha androids that appear human but lack emotions. Theyre seen as objects useful for labor or sex work, just human enough to not be strange but machine enough to not mistake them for people.
The story kicks into gear when Professor Allen Hobby (William Hurt) pitches taking Mecha to the next level: a machine that can love. That Mecha becomes David (Haley Joel Osment), an experimental Mecha designed to imprint on his owners and love them unconditionally, forever. And it works David is given to a grieving couple whose son is in suspended animation due to a rare disease. After some hijinks, hes accepted, until Martin, the boy hes filling in for, comes back.
Unfortunately Martin is cruel, and thanks to his manipulation, David is forced from home into a Pinocchio-esque journey to find a Blue Fairy and become a real boy. Through his eyes, we see a nihilistic theme-park vision of the future, where little is done to solve the still-looming climate apocalypse but neon cities and their pleasures boom.
The past: A.I. was released in 2001, but was originally going to come out long before that. Based on Brian Aldiss 1969 short story Supertoys Last All Summer Long, the film began as a Stanley Kubrick project in the 70s, languishing in development hell until the famed directors death in 1999. Steven Spielberg then took over, reportedly hewing closely to the plans Kubrick had for the film.
This meant that, at the time, the critical reception of A.I. largely revolved around its status as a strange hybrid of Kubrick and Spielbergs sensibilities, the last work of an idiosyncratic master carried out by one of his most prominent and stylistically different admirers. Most, like Roger Ebert, felt that the result was a frustrating film, attempting to parse where one mans vision ended and the others began.
But A.I. was an extremely fitting film for 2001, a year characterized by cinematic restlessness. Unsettling arthouse classics Donnie Darko and Mulholland Drive premiered. Shrek, which skewered Disney-style fairy tales with pop culture cynicism, also arrived, unwittingly laying the groundwork for surreal internet memes a decade later. Films that would spawn, extend, or hope to begin franchises floundered in every direction, with understated hits like Oceans Eleven arriving alongside strange blockbusters like Jurassic Park III and showstoppers like The Fellowship of the Ring.
No one knew what the 21st century would mean for movies, and a sad sci-fi fairytale about a robot boy created to stand in the void between a bleak future and an idyllic past could not have been a better match for the times.
The present: At first glance, the hedonistic carnival of A.I.s cities do not seem to hew terribly close to our current moment. Like a lot of cinematic futures, this one seems too loud, too garish, to ever be real. Jude Law as Gigolo Joe? The horrific robot bloodsport of the Flesh Fair, where obsolete robots battle to the death? We dont really have anything like that yet, right?
Only we do. The seeds of this future have already bloomed in our present. Its subtext is our subtext, a world formed by people with all the power afforded them by technology but none of the will to dream or love. The former would demand a clear-eyed response to shared crises looming ahead both at home and abroad; the latter would lead us to wield our innovations compassionately. Instead we have a world where algorithms reinforce biases and outrage is commodified, where every innovation is part and parcel with a new indignity. A lack of humanity that at every turn denies the option of a better future for all in favor of a more extravagant present for a few.
In A.I.s final 20 minutes, its revealed that this is the end of the world. In 2,000 more years, climate change will claim the last habitable portion of the Earth, and David will be the only one left who remembers humanity. Still a child, all David wants to remember is the human mother he imprinted on, but the viewer remembers everything else that the world was doomed by rage at the pending self-imposed disaster that humanity refused to face and instead directed toward the Mecha they created, the Mecha that would outlast them.
They made us too smart, too quick, and too many, Gigolo Joe, the Mecha sexbot that becomes Davids unlikely companion, says in one of his final scenes. In the end, all that will be left is us. Thats why they hate us
A.I. is refreshing because it is not interested in the question of whether or not we should create self-aware synthetic life, but instead asks what our responsibility toward it would be. If you can create a robot to love a human, one character asks early in the film, how do you get a human to love them back?
In the end it doesnt matter. Humanity doesnt even love itself enough to ensure its own survival.
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A.I. Artificial Intelligence shows us a future where we neglect to dream - The Verge
Where AI Meets Big Data, Innovation is Assured – Analytics Insight
Image Credit: NewtonX
When you work around the idea of the fourth industrial revolution, two technologies that make a quick flash into your brain are Big Data and Artificial Intelligence. A lot has been said and done in this regard. The combination of both has driven numerous industries towards success. While data or big data is considered the lifeblood of modern businesses, AI on the other hand is the heart to fill life into it.
Speaking more technically, we can say, big data is at the core of every business and the technology to harness its true value, AI is extremely essential to extract true meaning from data.
From Machine Learning to Computer Vision to Natural Language Processing, all AI subsets play a great role in getting something meaningful from just voluminous data.
According to a New Vantage survey report, the percentage of firms investing greater than US$50 million is up to 64.8% in 2020 from just 39.7% in 2018, with a total of 98.8% of firms investing in Big Data and AI initiatives. However, the pace of investment is leveling off, as only 51.9% of firms are accelerating their rate of investment, in stark contrast to the 91.6% who were accelerating their pace of investment in 2019. This includes some of the biggest companies, including Google, JP Morgan Chase, Glaxo Smith Kline, and Bank of America, among others.
The rising stars and the tech giants all have developed mastery at the intersection where big data meets AI.
This convergence of big data and Artificial Intelligence is what the MIT Sloan Management Review called the single most important development that is shaping the future of how firms drive business value from their data and analytics capabilities. These organizations understand how to combine data-savvy and strong AI capabilities into strongly differentiated solutions with massive market value. Here are a few ways data and AI empower each other:
Big data, the massive data collections that were all contributing to every day, is only getting bigger. Its estimated that by 2020, every person on earth will generate 1.7 MB of data every second, according to DOMO. Within that data, if we know how to unlock it, lies the potential to build amazing new businesses and solve some of the worlds greatest challenges.
Data is the fuel that powers AI, and large data sets make it possible for machine learning applications (machine learning is a branch of AI) to learn independently and rapidly. The abundance of data we collect supplies our AIs with the examples they need to identify differences, increase their pattern recognition capabilities, and see the fine details within the patterns.
AI enables us to make sense of massive data sets, as well as unstructured data that doesnt fit neatly into database rows and columns. AI is helping organizations create new insights from data that was formerly locked away in emails, presentations, videos, and images.
Databases are becoming increasingly versatile and powerful. In addition to traditional relational databases, we now have powerful graph databases that are more capable of connecting data points and uncovering relationships, as well as databases that specialize in document management.
To understand further lets look at some of the most common yet revolutionary applications of AI and Big Data driving business innovation.
With so many options on their fingertips, todays consumers are amongst the most fickle that the world of business has ever witnessed. Deep learning, which is a subset of AI, is helping businesses to predict consumer behavior by recognizing voice and search patterns.
Similarly, Big Data, in conjunction with predictive analytics and learning algorithms, come up with offers for customers even before they realize the need for it. A fantastic example of this is Starbucks personalizing customer experience. Through its app, it uses Big Data and AI to recommend different types of caffeine based on the weather and the customers location and many other features.
Digital marketing is the byword of any company worth its salt. Along the same lines are SEO, generating leads, and conversion. Big Data and AI work in combination to provide better insights to companies and narrow down on the targeted consumers who are critical to digital marketing.
By making use of the latest analytic tools, businesses are also able to save a lot of money and have better conversions. Netflix, the live streaming giant, incorporates AI into its digital marketing, which has exponentially raised their subscriptions. This directly translates to increased revenue.
Not many of us can imagine starting the day without the help of virtual assistants or VAs. The most notable VAs are Siri and Alexa. The same is true for many businesses all around the globe. AI has helped decipher or at least make sense of Big Data, which makes the VAs more intelligent.
The reaches of Big Data and AI are seen in self-driving cars that drive on auto-pilot. Among them is the Tesla. But of course, it is no secret as the companys CEO has been openly propagating the immense ways in which Artificial intelligence will change the very course of history.
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Smriti is a Content Analyst at Analytics Insight. She writes Tech/Business articles for Analytics Insight. Her creative work can be confirmed @analyticsinsight.net. She adores crushing over books, crafts, creative works and people, movies and music from eternity!!
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Where AI Meets Big Data, Innovation is Assured - Analytics Insight
Propelling Data Analytics with the Power of Artificial Intelligence – Analytics Insight
Can your data talk intelligently? AI plugged into data management systems aims to do just that!
Intelligent analytics offers a classic approach to discover the hidden intelligence behind historical and real-time data. This myriad suite of analytical techniques and algorithms can parse mind-boggling amounts of data generated in real-time to discover the hidden gems that are often missed or go undetected by traditional statistical methods.
The methodology of mixing intelligence with analytics reaches far beyond. It erects the foundation in algorithmic methods removing any bias introduced by an individual analyst. Whats more, the sheer volume of data adds to the veracity and accuracy of the results, rather than causing an unnecessary air of confusion for the analyst.
An artificial intelligence (AI) and analytics platform encapsulate the means to derive untapped value from the wealth of information, data constantly generates. While advanced analytics helps enterprises to uncover insights on current business processes and even draw predictions from historical information silos, AI acts as a force multiplier on this data crunching by pledging machine learning capabilities into these data models.
The best artificial intelligence algorithms and analytics software leverage machine learning solutions into big data platform. This way they transform data into intelligent pieces of information, self-service data visualization dashboards, automation-ready capabilities to maximize revenue and operational efficiencies.
AI can actually transform data into an intelligent piece of Intelligence
1. Unearthing new insights from data analytics
Artificial Intelligence excels in finding hidden patterns and insights from large datasets which are often unseen from human eyes, this is done at an unprecedented speed and scale. AI-powered tools exist answering the questions about your enterprise operations, for instance, which operations cycle had the quickest turn-around in a specific quarter.
2. Deploy analytics to predict data outcomes
AI-powered algorithms analyze data from multiple sources offering predictions on an enterprises next strategic move. It can also deep dive into data to share insights about your customers letting you know about their preferences, and which marketing channels would be the best to target them.
3. Unifying data across Platforms
Artificial Intelligence unifies data captured from different sources and platforms, accelerating data-driven innovation across data science, business analytics and data engineering categories.
Data analytics software
Think business intelligence gathered from a data analytics software that identifies patterns and formulates data relationships. This paves way for actionable alerts, smart data discovery and interactive dashboards, using a comprehensive set of data analytics software on an enterprise-grade analytics platform.
Machine learning and predictive analytics platform
An able platform lets you analyze structured and unstructured big data stored in data management platforms and external sources. AI and open-source data analytics platforms combine open-source machine learning with self-service analytics and predictive analytics to achieve data intelligence.
Natural language processing and text mining
Unstructured data explains stories, sentiments, emotions of your customers, employees and stakeholders. NLP and Text mining extracts terms and concepts from brochures, legal documents, emailers, social media messages, videos, audio files, web pages to unlock the value hidden in unstructured text and yield valuable business insights.
Interactive visualizations
Data visualization is the graphic representation of data. Interactive data visualizations and rich interactive dashboards are the major takeaways from Intelligent Analytics helping enterprises know their data more personally.
AI solution for sentiment analysis
Intelligent data analytics helps an enterprise to understand and highlight what is the peoples perception on social networks and the web about its products and services. Intelligent analytics is thus a blessing to enterprises for targeted customer servicing, customer engagement and retention.
In crux, AI blended data analytics aims to make the enterprise more efficient and productive thereby increasing its brand loyalty, drive revenues and eliminate the need for manual data processing mechanisms. With customised business insights that are accessible and relatable to the most critical objectives of the enterprise, Intelligent Analytics is here to stay.
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Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.
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Propelling Data Analytics with the Power of Artificial Intelligence - Analytics Insight
2025 Projeaction: Artificial Intelligence (AI) Market 2020 to 2025: Industry Scope of the Research – Cole of Duty
This report additionally covers the effect of COVID-19 on the worldwide market. The pandemic brought about by Coronavirus (COVID-19) has influenced each part of life all inclusive, including the business segment. This has brought along a several changes in economic situations.
A report on Artificial Intelligence (AI) market compiled by Brand Essence Market Research provides a succinct analysis regarding the values and trends existing in the current business scenario. The study also offers a brief summary of market valuation, market size, regional outlook and profit estimations of the industry. Furthermore, the report examines the competitive sphere and growth strategies of leading players in the Artificial Intelligence (AI) market. Download Premium Sample of the Report: https://industrystatsreport.com/Request/Sample?ResearchPostId=597&RequestType=Sample
TheMajorPlayersCovered in this Report:Intel, Nvidia, Samsung Electronics, Xilinx, Micron Technology, IBM, Microsoft, Google, Amazon Web Services (AWS), Facebook, Baidu, Oracle, Salesforce, SAS, SAP, Others & More.
Reports include the following segmentation: By OfferingHardwareSoftwareServicesBy TechnologyMachine LearningNatural Language ProcessingContext-Aware ComputingComputer Visionby End-User IndustryHealthcareManufacturingAutomotiveAgricultureRetailSecurityHuman ResourcesMarketingLawFintechBy RegionNorth Americao U.S.o Canadao MexicoEuropeo UKo Franceo Germanyo Russiao Rest of EuropeAsia-Pacifico Chinao South Koreao Indiao Japano Rest of Asia-PacificLAMEAo Latin Americao Middle Easto Africa
Results of the recent scientific undertakings towards the development of new Artificial Intelligence (AI) products have been studied. Nevertheless, the factors affecting the leading industry players to adopt synthetic sourcing of the market products have also been studied in this statistical surveying report. The conclusions provided in this report are of great value for the leading industry players. Every organization partaking in the global production of the Artificial Intelligence (AI) market products have been mentioned in this report, in order to study the insights on cost-effective manufacturing methods, competitive landscape, and new avenues for applications.
Global Artificial Intelligence (AI)Market: Regional SegmentationFor further clarification, analysts have also segmented the market on the basis of geography. This type of segmentation allows the readers to understand the volatile political scenario in varying geographies and their impact on the global Artificial Intelligence (AI)market. On the basis of geography, the global market for Artificial Intelligence (AI)has been segmented into:
North America(United States, Canada, and Mexico)Europe(Germany, France, UK, Russia, and Italy)Asia-Pacific(China, Japan, Korea, India, and Southeast Asia)South America(Brazil, Argentina, Colombia, etc.)Middle East and Africa(Saudi Arabia, UAE, Egypt, Nigeria, and South Africa)
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Report Methodology:
The information enclosed in this report is based upon both primary and secondary research methodologies.
Primary research methodology includes the interaction with service providers, suppliers, and industry professionals. Secondary research methodology includes a meticulous search of pertinent publications like company annual reports, financial reports, and exclusive databases.
Table of Content:
Market Overview: The report begins with this section where product overview and highlights of product and application segments of the Global Artificial Intelligence (AI) Market are provided. Highlights of the segmentation study include price, revenue, sales, sales growth rate, and market share by product.
Competition by Company: Here, the competition in the Worldwide Global Artificial Intelligence (AI) Market is analyzed, By price, revenue, sales, and market share by company, market rate, competitive situations Landscape, and latest trends, merger, expansion, acquisition, and market shares of top companies.
Company Profiles and Sales Data: As the name suggests, this section gives the sales data of key players of the Global Artificial Intelligence (AI) Market as well as some useful information on their business. It talks about the gross margin, price, revenue, products, and their specifications, type, applications, competitors, manufacturing base, and the main business of key players operating in the Global Artificial Intelligence (AI) Market.
Market Status and Outlook by Region: In this section, the report discusses about gross margin, sales, revenue, production, market share, CAGR, and market size by region. Here, the Global Artificial Intelligence (AI) Market is deeply analyzed on the basis of regions and countries such as North America, Europe, China, India, Japan, and the MEA.
Application or End User: This section of the research study shows how different end-user/application segments contribute to the Global Artificial Intelligence (AI) Market.
Market Forecast: Here, the report offers a complete forecast of the Global Artificial Intelligence (AI) Market by product, application, and region. It also offers global sales and revenue forecast for all years of the forecast period.
Research Findings and Conclusion: This is one of the last sections of the report where the findings of the analysts and the conclusion of the research study are provided.
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Artificial Intelligence In Fashion Market Detailed Analysis of Current Industry Figures with Forecasts Growth By 2027 – CueReport
The Artificial Intelligence In Fashion Market report upholds the future market predictions related to Artificial Intelligence In Fashion market size, revenue, production, Consumption, gross margin and other substantial factors. It also examines the role of the prominent Artificial Intelligence In Fashion market players involved in the industry including their corporate overview. While emphasizing the key driving factors for Artificial Intelligence In Fashion market, the report also offers a full study of the future trends and developments of the market.
The global artificial intelligence in fashion market accounted for US$ 270.0 Mn in 2018 and is expected to grow at a CAGR of 36.9% over the forecast period 2019-2027, to account for US$ 4,391.7 Mn in 2027. Driving factors such as availability of massive amount of data due to increasing proliferation of digital services across the globe, and real time consumer behavior insights and increased operational efficiency are driving the adoption of AI in fashion industry will drive the market during the forecast period and have a high impact in the short term. However, factors such as concerns related to data privacy and security is anticipated to hinder the market growth in the coming years.
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The overall artificial intelligence in fashion market size has been derived using both primary and secondary source. The research process begins with exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the artificial intelligence in fashion market. It also provides the overview and forecast for the global artificial intelligence in fashion market based on all the segmentation provided with respect to five major reasons such as North America, Europe, Asia-Pacific, the Middle East and Africa, and South America. Also, primary interviews were conducted with industry participants and commentators in order to validate data and analysis. The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers, and national sales managers, and external consultant such as valuation experts, research analysts and key opinion leaders specializing in the artificial intelligence in fashion market. Some of the players present in artificial intelligence in fashion market are Adobe Inc., Amazon Web Services, Inc., Catchoom Technologies S.L., Facebook, Inc., Google LLC, Huawei Technologies Co., Ltd., IBM Corporation, Microsoft Corporation, Oracle Corporation, and SAP SE among others.
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AI integration in fashion plays a crucial role in sales, marketing, and customer-focused purposes. Initial adopters point toward the key impacts of technology in improving customer experience and decent growth in company revenue. Elevated customer experience helps the retailer to crack entirely new tactics of customer engagement and communication. With AI integration, the retailers can precisely spot the customers expected needs at precise times and offer the appropriate product to gain a competitive advantage. Some of the past initiatives taken in the fashion industry sector which has revolutionize the use of AI in the sector are North Face leveraging IBM Watsons ML technology to recommend more personalized apparel to the customers. Further, eBays AI integration helps their sellers sell more by better inventory management and pricing recommendations. Also, several fashion brands are leveraging chatbot to improve the customer experience; Tommy Hilfigers Facebook Messenger chatbot offers consumers a custom-made and interactive shopping experience than customary online shopping. Besides, Sephora, Amazon, Target, ASOS, Stitch Fix, and Olay are other renowned fashion industry names in the list who have already integrated the AI solution to boost their sales and marketing strategy.
Global Artificial Intelligence In Fashion Market: Drivers and Restraints: This section of the Artificial Intelligence In Fashion Market Analysis report we are covering various drivers and restraints that have affected the global Artificial Intelligence In Fashion market. The complete study of plentiful drivers of the market enables market professionals to get a clear viewpoint of the Artificial Intelligence In Fashion market share, which consists of Artificial Intelligence In Fashion industry environment, advancement market, product innovations, latest developments, and Artificial Intelligence In Fashion market risks.
The artificial intelligence in fashion market has been segmented on the basis of offerings, deployment, application, end-user industry, and geography. The artificial intelligence in fashion market based on offerings is sub-segmented into solution and services. The solution segment is expected to hold the prime market share in the artificial intelligence in fashion market. The artificial intelligence in fashion market on the basis of deployment is segmented into cloud and on-premise. The cloud segment led the artificial intelligence in fashion market and it is anticipated to continue its dominance during the forecast period. The market for artificial intelligence in fashion by application is further segmented into product recommendation, virtual assistant, product search and discovery, creative designing and trend forecasting, customer relationship management, and others. The product recommendation segment led the artificial intelligence in the fashion market in 2018 and is expected to continue its dominance during the forecast period.
A Pin-point overview of TOC of Artificial Intelligence In Fashion Market are:
Overview and Scope of Artificial Intelligence In Fashion Market
Artificial Intelligence In Fashion Market Insights
Industry analysis - Porter's Five Force
Company Profiles
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Artificial Intelligence In Fashion Market Detailed Analysis of Current Industry Figures with Forecasts Growth By 2027 - CueReport