Who Should be Responsible when Robots and AI Cause Accidents

Who should be considered lawfully responsible when a self-driving vehicle hits a walker? Should the finger be pointed at the car proprietor, manufacturers or the engineers of the artificial intelligence (AI) software that drives the vehicle?

The question of deciding ‘risk’ for decision making achieved by robots or artificial intelligence is an intriguing and significant subject as the usage of this innovation increases in the industry, and starts to all the more directly sway our everyday lives.

To be sure, as applications of Artificial Intelligence and machine learning innovation develops, we are probably going to observe how it changes the idea of work, organizations, businesses and society. But, in spite of the fact that it has the ability to disrupt and drive more prominent efficiencies, AI has its snags: the issue of ‘who is at risk when something goes astray’ being one of them.

Road traffic incidents are one of the main causes of accidental death and are one of the leading supporting contentions for why self-driving vehicles should be adopted more rapidly. Struggles to allocate risk for mishaps including cutting-edge advancements like AI have ruled worldwide conversations even as preliminaries are progressing in numerous places far and wide for example, in Singapore, South Korea and Europe.

During the third annual edition of the TechLaw. Fest forum held recently, experts said that Singapore’s laws are presently incapable to adequately appoint risk on account of misfortunes or damage endured in accidents including AI or robotics technology.

The extraordinary capacity of autonomous robots and AI frameworks to work independently with no human contribution muddies the waters of liability,” said Charles Lim, co-chair of the Singapore Academy of Law’s Subcommittee on Robotics and Artificial Intelligence.

The 11-part Robotics and Artificial Intelligence Sub-panel had recently published its report on what should be possible to set up civil liability in such cases a month ago. Singapore, regardless of positioning first in the 2019 International Development Research Center’s Government Artificial Intelligence Readiness Index, actually doesn’t have law frameworks set up for overseeing liability stemming from robots or AI, including civil liability for autonomous vehicles (AVs).

#artificial intelligence #latest news #robotics

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Buddha Community

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

• Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
• Slowing Down the Weight Norm Increase in Momentum-based Optimizers
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
• Coherent Reconstruction of Multiple Humans from a Single Image
• Variational Bayesian Monte Carlo with Noisy Likelihoods
• GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
• Are we done with ImageNet?
• How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds
• Background Modeling via Uncertainty Estimation for Weakly-supervised Action Localization
• The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps
• Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
• Learning a Unified Sample Weighting Network for Object Detection

Articles

• Deepfake Detection Challenge Results: An open initiative to advance AI
• Slightly Unnerving AI Produces Human Faces Out of Totally Pixelated Photos
• NeoML Released as TensorFlow Alternative
• Google Meet takes on Zoom with AI-powered noise cancellation
• Open Compound Domain Adaptation
• Dense pose for animal classes with transfer learning
• 10 Ways Enterprises Are Getting Results From AI Strategies
• An understanding of AI’s limitations is starting to sink in
• Why we switched from Flask to FastAPI for production machine learning
• Simple Transformers
• The key differences between rule-based AI and machine learning
• Deep learning-based surrogate models outperform simulators and could hasten scientific discoveries
• HANDS-ON GUIDE TO OBJECT DETECTION USING YOLO
• Artificial neural networks that mimic the flexibility and computing power of the brain
• MIT researchers train AI to predict how humans paint works of art
• What Do Machine Learning Engineers Do Anyway?
• AI Is Amazing But Complicated, And We Don’t Necessarily Need To Plunge In Headfirst
• Deep learning networks track up to 100 animals simultaneously
• Is artificial intelligence changing art?

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Amsterdam And Helsinki Launch Open AI Registers

Amsterdam and Helsinki both launched an Open AI Register at the Next Generation Internet Summit. According to sources, these two cities are the first in the world that are aiming to be open and transparent about the use of algorithms and AI in the cities.

Currently, in the beta version, Algorithm Register is an overview of the artificial intelligence systems and algorithms used by the City of Amsterdam. The register is an effort to show where the cities are currently making use of AI and how the algorithms work.

Jan Vapaavuori, Mayor of Helsinki stated, “Helsinki aims to be the city in the world that best capitalises on digitalisation. Digitalisation is strongly associated with the utilisation of artificial intelligence. With the help of artificial intelligence, we can give people in the city better services available anywhere and at any time. In the front rank with the City of Amsterdam, we are proud to tell everyone openly what we use Artificial Intelligence for.”

#news #ai register #amsterdam ai #helsinki ai #open ai register #ai

Unlocking the Power of Data And AI for Businesses

The latest reports show that the Artificial Intelligence market was worth $40.2 billion in 2020. Experts attribute this growth to the increasing business adoption of AI. Data is the biggest asset that a company has in 2021. A comprehensive data strategy ensures the company can derive valuable insights that enable better decision-making.

What is the role of AI in business? There are countless examples of AI in business for forecasting and predicting values. For example, AI solutions enable financial companies to oversee stock price fluctuations and take advantage of trading opportunities. MetaStock is one such solution that provides analytical tools based on AI and data to predict stock prices.

Another approach of data and analytics that companies take is to forecast their budget, expenses, and revenues. It involves using historical data and transactional patterns to identify a company’s budget. Big data provides solutions to specific business problems through Artificial Intelligence models.

This article will focus on the following topics – 

• AI capabilities in business
• What is intelligent analytics?
• Latest trends in data analytics
• Business cases for AI

Let’s have a look at these topics in detail.

AI Capabilities in Business 

Harvard Business Review suggests that AI can do three significant things for your business. It can – 

1. Automate business processes
2. Gain valuable insights
3. Improve customer engagement

When people ask, “Why is data important for AI,” the answer is that all AI solutions require data. The foundation of AI analytics is historical or real-time data. With the help of data, AI can deliver the above three capabilities.

AI enables business process automation with the help of Robotic Process Automation. RPA bots can work with triggers, while smart bots can identify things that need to stay or eliminate during automation. 

The biggest role of AI in business is to generate and deliver valuable insights. When there’s tons of data, patterns are bound to emerge. The power of data in business is brought to its best use when companies can use any kind of data to improve business results. It usually happens by finding opportunities that are only seen through data and analytics. 

Artificial Intelligence drives customer experience by learning from the environment. Smart bots and AI algorithms deliver better customer service by understanding the conversation in real-time. Several companies use AI customer experience bots to enhance service delivery to their audience.

Read More: A CTO’s guide for AI Implementation

What is Intelligent Analytics?

Intelligent analytics is a branch of Artificial Intelligence. Companies use intelligent data and analytics to uncover meaningful insights. The purpose is to derive value from collected business data.

INSIGHTS – that’s the keyword here. Intelligent analytics is a part of the overall data strategy. It showcases the business that even their top management might sometimes miss. It includes new market segment opportunities, product differentiation parameters, honest product feedback, actual growth rate or products, expense and income status for a particular time period, and much more. 

There are three different types of intelligent analytics – 

• Descriptive analytics
  It focuses on using historical and real-time data to identify what happened during a time period. Examples of AI in business for descriptive analytics include understanding the new customers acquired, sales during a period, and more.
• Predictive analytics
  It focuses on predicting future values for the company. The Machine Learning algorithm uses past values and identifies values that are likely to occur in the future. Examples include predicting the budget for the next year, identifying the number of potential customers, and more.
• Prescriptive analytics
  It focuses on determining the right path that companies should take based on the insights generated from data. It highlights the action steps that will bring the desired results. Examples include price changes based on the historical demand of a product. 

Latest Trends for Data Analytics

As companies move towards modern applications of data analytics, everyone asks the same questions – “How can I use AI in my business?”. We will address the answer in the next section. For now, let’s understand the latest trends in data analytics that make AI possible for businesses. 

1. AI SaaS
   Data solutions are increasingly available as Software as a Service product. Companies can use off-the-shelf software to identify patterns by inputting data in different columns. There are software-based on multiple business functions, while some are common for all processes. However, if you want an enterprise-grade analytical tool, custom AI solutions are more effective.
2. Natural Language Processing
   AI machines are now learning on their own. More and more companies are experimenting with unstructured data through natural language processing. Intelligent bots now have conversations as real as humans do. It brings a new age of AI systems that can interact using emotions, feelings, and everything that humans have.
3. Extended Reality
   Data analytics combined with extended reality is generating unfathomable results. Through AR and VR, companies are creating virtual simulations – but combining it with the AI capabilities for predicting future environments, opportunities and making plans to understand how to solve future challenges. Extended reality delivers an AI customer experience that highly improves the customer satisfaction rate.
4. Interactive Visualizations
   Intelligent analytics is simplifying data representation for companies. There are interactive dashboards through which all stakeholders can better understand the data. It gives a personalized view of all the data points and showcases how it is performing. Interactive visualizations are useful for identifying visual patterns that might be missed otherwise.

Business Cases for AI

Today, there are use cases of AI in almost every business process. Even in the manufacturing sector, intelligent data analytics can help build systems that can identify the maintenance schedule of equipment – keeping it safe from breakdowns. 

There are plenty of applications that are common in business. We will talk about these popular business use cases of AI, based on different functions –

• Customer Service
  Sentiment analysis is a useful application of data analytics in business. Companies can identify what customers are saying about their product. It also includes:
  
  – Customer responses
  – Call forwarding
  – Call classification
  – Intelligent routing
• Marketing
  Marketing analytics is the most significant use. It helps to determine the results that we can achieve from the marketing budget. It also includes:
  
  – Personalized marketing
  – Intelligent content
  – Contextual marketing
  – Budget forecasting
• Sales
  The sales team can identify the future sales potential by analyzing the historical data. It helps to find areas where activities need to be improved. It also includes:
  
  – Predictive sales
  – Sales chatbot
  – Appointment scheduling
  – Lead management
• Operations
  The role of AI in business operations is increasing dramatically. Companies use automation and data analytics to drive valuable insights. It also includes:
  
  – Data mining
  – Predictive maintenance
  – Operational analytics
  – Inventory optimization

Apart from this, there are various business cases in different domains like healthcare, FinTech, HR, Insurance. In every domain and area of business, AI is showing significant results.

Checkout: The Future of Artificial Intelligence Services for Business

Bottom Line

Artificial Intelligence is transforming how companies used to work. Today, intelligent analytics is a business priority. Since data is the biggest asset they have, companies want to leverage AI and derive valuable insights from that data. That’s why more and more businesses are now hiring AI developers to build intelligent solutions.

BoTree Technologies is a leading Artificial Intelligence and Data Analytics company in the US, India, and Singapore.

Contact us today to understand how we can build AI capabilities for your business.

 

Source: https://www.botreetechnologies.com/blog/unlocking-the-power-of-data-and-ai-for-businesses/

#artificial intelligence #ai #ai solutions #ai in business #ai capabilities in business #robotic process automation