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This document discusses big data and analytics. It notes that digital data is growing exponentially and will reach 35 zettabytes by 2020, with 80% coming from enterprise systems. Big data is being driven by increased transaction data, interaction data from mobile and social media, and improved processing capabilities. Major players in big data include Google, Amazon, IBM and Microsoft. Traditional analytics struggle due to batch processing and lack of business context. The document introduces OpTier's approach of capturing real-time business context across interactions to enable insights with low costs and flexibility. Potential use cases for financial services are discussed.
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Cloud, mobile and big data have, together, changed the very texture of traditional IT services and programming development.
A great many old IT and tech occupations around the globe confront imminent extinction, thus, rest guaranteed, birthing numerous new ones.
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4. Data-driven decision making exploding as people can more easily access and explore data to improve outcomes.
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Working with the right AI company can help streamline the business operations, optimize the resources, and increase returns by changing the way management and employees perform their day-to-day activities at work.
Here are the top 20 artificial intelligence companies to watch out for in 2022:-
https://www.datatobiz.com/blog/top-artificial-intelligence-companies/
Data Culture and the Future of Analytics #CIAEX Exchange Jan 2016Jonathan Woodward
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Neo4j Aura Enterprise is a fully-managed graph database platform as a service that provides flexible deployment models, lightning fast performance at scale, and 24/7 support for mission critical applications. It offers innovative ways to find insights in data, is trusted by thousands of customers worldwide, and is effortless to use with zero administration needed.
This document discusses strategies for effective data monetization. It outlines challenges in data monetization like the increasing volume of data and the need for AI. It presents a data monetization maturity model and describes the top 5 best practices for successful data monetization as: getting the foundation right by infusing AI/data science; focusing on people like data engineers and scientists; constructing a robust business model; and ensuring trust and ethics. The document recommends using case generation and prioritization and provides industry examples. It promotes IBM Cloud Private for Data as an integrated analytics platform to overcome challenges and realize the benefits of data monetization.
Business intelligence with web data gabc maySemetis
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Machine learning is a core area of artificial intelligence that allows computer programs to learn from data without being explicitly programmed. The document discusses the impact of machine learning across various industries like marketing, healthcare, manufacturing and financial services. It also profiles major players using machine learning and outlines how consulting groups are helping clients implement machine learning strategies and programs.
Artificial intelligence is becoming increasingly important for businesses. It can automate tasks like customer service, improve marketing through personalized experiences, and help predict outcomes. As more companies develop new AI technologies, those that don't adopt AI may struggle to keep up with competitors in terms of productivity and efficiency. The document provides several examples of how businesses are using AI for tasks like operational automation, predictive maintenance, fraud prevention, and more. It concludes that AI offers businesses many benefits and opportunities for growth.
1) AI is currently experiencing a "big AI Spring" due to improvements in data availability, processing power, and interfaces that have increased data for training models.
2) However, there is also significant hype around AI capabilities that often misrepresent the current state of the technology. AI systems require specific, high-quality data and focused problems to solve in order to deliver real value.
3) The speaker advocates focusing on using AI to empower employees and improve customer experiences, rather than replacing humans, in order to realize transformational benefits while managing expectations.
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
The fear of robots taking over our lives has been a prevalent concern, with over 70% of the U.S. population expressing apprehension, as highlighted by a 2017 Pew Research study. However, while the emergence of a Skynet-like scenario remains uncertain, it's evident that technology, particularly artificial intelligence (AI), is poised to revolutionize various aspects of our daily tasks, freeing us from repetitive and dehumanizing job elements rather than rendering us obsolete. With AI being a strategic priority for 84% of businesses, its implementation has shown remarkable efficiency enhancements, such as boosting sales team productivity by over 50%. The accessibility of AI tools has expanded significantly, enabling practically anyone to leverage its benefits. In this discourse, we'll explore 20 diverse real-world applications of AI, ranging from healthcare and finance to entertainment and government, illustrating its pervasive impact on modern society.
10 Business Functions That Are Ready To Use Artificial IntelligenceBernard Marr
Artificial intelligence (AI) and machine learning are starting to be adopted by businesses across nearly every industry even though it's still a new technology, and there's no way of knowing all that it will enable us to do once it's matured. Here are 10 business functions that are ready to use artificial intelligence.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
20 Useful Applications of AI Machine Learning in Your Business ProcessesKashish Trivedi
A 2017 study from Pew Research found that more than 70% of the U.S. is scared that robots are going to take over our lives. And, while we can’t perfectly predict the emergence of a Skynet singularity, we can say with some certainty that technology is set to take over the repetitive, dehumanizing elements of our jobs instead of putting us out of work. Artificial intelligence (AI) is a strategic priority for 84% of businesses, and in some cases has been used to improve sales team efficiency by over 50%. Even I’ve used AI in the past to generate hundreds of relevant hashtags for social media posts at the click of a button. It was once the stuff of utopian science fiction and huge enterprises, but now practically anyone can take advantage. For this post, we will dive into 20 different applications of AI in the real world.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
A Guide on How AI Contributes to Businesses in Today’s Era to Watch in 2023.Techugo
Artificial Intelligence and Machine Learning have become the main focus of the scene. Artificial intelligence can be used for a wide variety of uses in business, including streamlining processes and aggregating the performance of companies. Researchers are still determining what AI will mean for businesses shortly. AI is predicted to shift technological advancement away from the traditional two-dimensional screen and towards the three-dimensional physical space surrounding the person.
Although the acceptance by society in general for AI does not mean anything new. The idea itself isn’t. Artificial intelligence is a broad field of business application. Indeed, most of us interact with AI in some way or another. Artificial Intelligence is changing all aspects of business across every industry. To know more, visit the post.
Our new perspective on achieving the full potential of human and artificial intelligence.
By Fjord, design and innovation from Accenture Interactive, and Accenture The Dock.
How Can Businesses Adopt AI Technology to Achieve Their GoalsKavika Roy
https://www.datatobiz.com/blog/businesses-adopt-ai-technology/
Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. The global AI market is worth $327.5 billion in 2021.
However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc.
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI automates tasks to save money and makes recommendations. In the long term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
Allaboutailuminarylabsjanuary122017 170112151616Quang Lê
Artificial intelligence is promising new technologies but also hype that needs separating from reality. A discussion was held between executives in healthcare, machine learning and analytics with experts Hilary Mason and Sandy Allerheiligen. In the short term, AI can automate tasks to save money and make recommendations. In the longer term, AI will transform industries like healthcare through medical imaging analysis and self-driving cars. Companies should start with problems not solutions, emphasize how AI augments not replaces humans, and engage skeptics to gain support.
Artificial intelligence in the apparel industryThreadSol
The document discusses how artificial intelligence can be adopted across the apparel industry, from using AI to personalize online shopping experiences and recommend products, to implementing reinforcement learning to make manufacturing processes more efficient, to leveraging AI for data analysis and automating communications. However, it also notes some shortcomings of AI, such as issues around data privacy and control as well as the current high costs associated with incorporating AI technologies.
How To develop An Artificial Intelligence Strategy: 9 Things Every Business M...Bernard Marr
An artificial intelligence (AI) strategy has become a vital tool every organisation needs. Based on my experience helping companies develop their AI strategies, I share my nine things every AI strategy must include.
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### Data Description and Analysis Summary for Presentation
#### 1. **Importing Libraries**
Libraries used:
- `pandas`, `numpy`: Data manipulation
- `matplotlib`, `seaborn`: Data visualization
- `scikit-learn`: Machine learning utilities
- `statsmodels`, `pmdarima`: Statistical modeling
- `keras`: Deep learning models
#### 2. **Loading and Exploring the Dataset**
**Dataset Overview:**
- **Source:** CSV file (`mumbai-monthly-rains.csv`)
- **Columns:**
- `Year`: The year of the recorded data.
- `Jan` to `Dec`: Monthly rainfall data.
- `Total`: Total annual rainfall.
**Initial Data Checks:**
- Displayed first few rows.
- Summary statistics (mean, standard deviation, min, max).
- Checked for missing values.
- Verified data types.
**Visualizations:**
- **Annual Rainfall Time Series:** Trends in annual rainfall over the years.
- **Monthly Rainfall Over Years:** Patterns and variations in monthly rainfall.
- **Yearly Total Rainfall Distribution:** Distribution and frequency of annual rainfall.
- **Box Plots for Monthly Data:** Spread and outliers in monthly rainfall.
- **Correlation Matrix of Monthly Rainfall:** Relationships between different months' rainfall.
#### 3. **Data Transformation**
**Steps:**
- Ensured 'Year' column is of integer type.
- Created a datetime index.
- Converted monthly data to a time series format.
- Created lag features to capture past values.
- Generated rolling statistics (mean, standard deviation) for different window sizes.
- Added seasonal indicators (dummy variables for months).
- Dropped rows with NaN values.
**Result:**
- Transformed dataset with additional features ready for time series analysis.
#### 4. **Data Splitting**
**Procedure:**
- Split the data into features (`X`) and target (`y`).
- Further split into training (80%) and testing (20%) sets without shuffling to preserve time series order.
**Result:**
- Training set: `(X_train, y_train)`
- Testing set: `(X_test, y_test)`
#### 5. **Automated Hyperparameter Tuning**
**Tool Used:** `pmdarima`
- Automatically selected the best parameters for the SARIMA model.
- Evaluated using metrics such as AIC and BIC.
**Output:**
- Best SARIMA model parameters and statistical summary.
#### 6. **SARIMA Model**
**Steps:**
- Fit the SARIMA model using the training data.
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Indicates accuracy on training data.
- **Test MAE:** Indicates accuracy on unseen data.
- **Train RMSE:** Measures average error magnitude on training data.
- **Test RMSE:** Measures average error magnitude on testing data.
#### 7. **LSTM Model**
**Preparation:**
- Reshaped data for LSTM input.
- Converted data to `float32`.
**Model Building and Training:**
- Built an LSTM model with one LSTM layer and one Dense layer.
- Trained the model on the training data.
**Evaluation:**
- Evaluated on both training and testing sets using MAE and RMSE.
**Output:**
- **Train MAE:** Accuracy on training data.
- **T
Airline Satisfaction Project using Azure
This presentation is created as a foundation of understanding and comparing data science/machine learning solutions made in Python notebooks locally and on Azure cloud, as a part of Course DP-100 - Designing and Implementing a Data Science Solution on Azure.
Nehru Place @ℂall @Girls ꧁❤ 9873940964 ❤꧂VIP Jina Singh Top Model Safe
Big Data LDN 2017: Reshaping Digital Business With Augmented Intelligence
1. Reshaping Digital Business with Augmented Intelligence
16th November
Robert Golladay
Managing Director, Europe, CognitiveScale
golladay@cognitivescale.com
5. Three Major AI Caliber Categories
• Artificial Narrow Intelligence (ANI). Also known as Weak AI
• We are currently a world (basically) running on ANI
• Cars are full of ANI. It can fine tune your fuel injection (but it cant give you dating advice)
• The world’s best chess and Othello players are ANI
• Analysis of a mammogram is done by ANI more accurately and faster than a human. When AI starts beating us, it never looks back
• Artificial General Intelligence (AGI). Also known as Strong AI . Breadth . All of this could happen soon
• Very few are super skeptical of “if” . It’s when – “Why wouldn’t it just own the economy?”
• Recursive self-improvement. A machine that can perform any intelligent task that a human can. It could happen faster than we think
• Artificial Super Intelligence (ASI). Musk and Open AI and Neuralink to combat. AI that’s smarter than any human,
across the board
By 2025 ANI will be everywhere – “Cook vs Chef”
Tim Urban, The Road to Superintelligence
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6. ANI is Here; What About AGI and ASI?
Why the Pursuit? ASI could allow us to conquer our mortality.
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10. Super convergence powered disruption is already underway
• World’s largest taxi company has no taxis (Uber)
• Largest accommodation provider owns no real estate (Airbnb)
• Largest phone companies own no telco infra (Skype, WeChat)
• World’s most valuable retailer has no inventory (Alibaba)
• Most popular media owner creates no content (Facebook)
• World’s largest movie house owns no cinemas (NetFlix)
These are ALL technology companies that engage customers brilliantly!
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29. Our AI Products are proven
• 42% increase in shopper engagement, 21% increase in conversion
• 8x increase in wealth advisor productivity
• 32% drop in trouble tickets and 50% increase in resolution time
• 8% drop in claims denial, 15% drop in claims administration costs
• > 90% invoice matching confidence rating
Our clients are global market leaders
• World’s largest department store
• World’s largest cancer center
• Worlds largest non-profit healthcare system
• World’s largest oil & gas company (Fortune #2)
• World’s second largest telecommunications company
• World’s most valuable bank
* Deloitte and IDC estimate market of Enterprise AI to be $40B by 2020 7
Strong investors and $50m+ in funding
Company Profile