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How Technology can help India’s Traditional Craftspeople

Written by : Suki Iyer on  Digilah (Tech Thought Leadership)

A recent conversation with a friend got me thinking of the intersection between technology, design, the preservation and flourishing of traditional handicrafts, and communities. 

The Indian handicraft industry is a highly labor intensive one, with more than 7 million artisans, a majority of whom are women and largely underprivileged.

This industry, which is traditionally a major source of revenue generation in rural India, has been in decline (though there have been several efforts to support it), and has been hit hard by the pandemic as well. 

What are the glaring gaps in the market for traditional craft? (specific to India, but this could apply to the world as well). To my mind the key gaps are in design, and in business building capacities

Local artisans lack the ability to meet the needs of new markets and are forced to find low unskilled employment in urban industries. One of the major factors contributing to this is that artisans are not trained to contemporize their designs. 

In this article, I’d like to focus on design and the role technology can play in meeting the current gaps. 

While some work has been done on modernizing design, a lot of craft continues to center around traditional design, often not appealing to modern sensibilities, and thus not being able to build the foundation of a sustainable business. How can technology help? For example, AI techniques have been leveraged for emulating creativity and imagination – for image generation, style-transfer, image-to-image translation; for pattern generation, and color-transfer etc.  

An interesting study (Raviprakash et al., May 2019) describes how AI techniques can be used to contemporize design, while keeping the underlying technique unchanged. It generated colored motifs and patterns that can be manufactured into physical products. This study experimented with using AI on the popular IKAT weave. Unlike other dyeing techniques, in IKAT the yarn is dyed BEFORE it is woven. This is what gives it its unique shading effect. This property was harnessed by the researchers to create a contemporary design. 

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The researchers first used a black motif using an AI technique trained on a set of 1000 paintings from a famous European painter, Piet Mondrian, and their gray-scale counterparts. The simplicity of these paintings along with the use of only primitive colors made them an ideal choice for our approach, since the model is able to learn primitive colorization of a motif from a relatively small training dataset. 

The model used a generator which colorizes the input and a discriminator that learns to distinguish between the real paintings and the colorized images. The discriminator’s output determines the loss of the generator, which the generator tries to minimize, effectively colorizing images to make them indistinguishable from real paintings. 

These motifs were re-colored with colors of an inspiration image using a statistical approach of global color transformation, and the design was post-processed to a grid that could be readily used for dyeing, as each cell is of a single color. 

Products manufactured with designs generated using the above approach are found to be much more visually appealing than their traditional counterparts in the present market. Local artisans used these designs to manufacture and sell products successfully. A person painting a picture Description automatically generated with medium confidence

There are several such examples of how technology can modernize craft without compromising on the underlying uniqueness of a particular craft technique. 

Investments need to be made in building such design capacity amongst artisans so they can once again take their place as valued centers of their communities. 

Suki Iyer

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AI Tech

5 Levels of Autonomy in Vehicles

Witten by Oliver-Werner K. on Digilah (Tech Thought Leadership)

Levels 0 to 5

Level 0 – No Automation. The human at the wheel steers, brakes, accelerates, and negotiates traffic.

Level 1 – Driver Assistance. …

Level 2 – Partial Automation. …

Level 3 – Conditional Automation. …

Level 4 – High Automation. …

Level 5 – Full Automation.

Researchers forecast that by 2025 we’ll see approximately 8 million autonomous or semi-autonomous vehicles on the road. Before merging onto roadways, self-driving cars will first have to progress through 6 levels of driver assistance technology advancements.

What exactly are these levels? And where are we now? 

The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous). These levels have been adopted by the U.S. Department of Transportation. 

Level 0 (No Driving Automation)

Most vehicles on the road today are Level 0: manually controlled. The human provides the dynamic driving task although there may be systems in place to help the driver. An example would be the emergency braking system―since it technically doesn’t “drive” the vehicle, it does not qualify as automation. 

Level 1 (Driver Assistance)

This is the lowest level of automation. The vehicle features a single automated system for driver assistance, such as steering or accelerating (cruise control). Adaptive cruise control, where the vehicle can be kept at a safe distance behind the next car, qualifies as Level 1 because the human driver monitors the other aspects of driving such as steering and braking. 

Level 2 (Partial Driving Automation)

This means advanced driver assistance systems or ADAS. The vehicle can control both steering and accelerating/decelerating. Here the automation falls short of self-driving because a human sits in the driver’s seat and can take control of the car at any time. Tesla Autopilot and Cadillac (General Motors) Super Cruise systems both qualify as Level 2.

Level 3 (Conditional Driving Automation)

The jump from Level 2 to Level 3 is substantial from a technological perspective, but subtle if not negligible from a human perspective.

Level 3 vehicles have “environmental detection” capabilities and can make informed decisions for themselves, such as accelerating past a slow-moving vehicle. But―they still require human override. The driver must remain alert and ready to take control if the system is unable to execute the task.

Almost two years ago, Audi (Volkswagen) announced that the next generation of the A8―their flagship sedan―would be the world’s first production Level 3 vehicle. And they delivered. The 2019 Audi A8L arrives in commercial dealerships this Fall. It features Traffic Jam Pilot, which combines a lidar scanner with advanced sensor fusion and processing power (plus built-in redundancies should a component fail).

However, while Audi was developing their marvel of engineering, the regulatory process in the U.S. shifted from federal guidance to state-by-state mandates for autonomous vehicles. So for the time being, the A8L is still classified as a Level 2 vehicle in the United States and will ship without key hardware and software required to achieve Level 3 functionality. In Europe, however, Audi will roll out the full Level 3 A8L with Traffic Jam Pilot (in Germany first). 

artificial intelligence

Level 4 (High Driving Automation)

The key difference between Level 3 and Level 4 automation is that Level 4 vehicles can intervene if things go wrong or there is a system failure. In this sense, these cars do not require human interaction in most circumstances. However, a human still has the option to manually override.

Level 4 vehicles can operate in self-driving mode. But until legislation and infrastructure evolves, they can only do so within a limited area (usually an urban environment where top speeds reach an average of 30mph). This is known as geofencing. As such, most Level 4 vehicles in existence are geared toward ridesharing. For example:

NAVYA, a French company, is already building and selling Level 4 shuttles and cabs in the U.S. that run fully on electric power and can reach a top speed of 55 mph.

Alphabet’s Waymo recently unveiled a Level 4 self-driving taxi service in Arizona, where they had been testing driverless cars―without a safety driver in the seat―for more than a year and over 10 million miles.

Canadian automotive supplier Magna has developed technology (MAX4) to enable Level 4 capabilities in both urban and highway environments. 

They are working with Lyft to supply high-tech kits that turn vehicles into self-driving cars.Just a few months ago, Volvo and Baidu announced a strategic partnership to jointly develop Level 4 electric vehicles that will serve the robotaxi market in China.

Level 5 (Full Driving Automation)

Level 5 vehicles do not require human attention―the “dynamic driving task” is eliminated. Level 5 cars won’t even have steering wheels or acceleration/braking pedals. They will be free from geofencing, able to go anywhere and do anything that an experienced human driver can do. Fully autonomous cars are undergoing testing in several pockets of the world, but none are yet available to the general public!

 

(Source1: https://www.synopsys.com/automotive/autonomous-driving-levels.html)

(Source2: https://newsroom.intel.com/news/autonomous-driving-hands-wheel-no-wheel-all/)

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AI Tech Web 3.0 Tech

Web3.0:The Real decentralized Internet 

Written by Femi Omoshona on Digilah (Tech Thought Leadership)

Decentralized technology is the present and the early we start investing our time, energy and resources trying to understand what future DApp looks like the better for us. 

Blockchain, AI, AR and IOT are amazing technologies we should be wrapping our brain around in this 21st century.

In this article, I lay out how the web has evolved, where it’s going next, and how Africa as a continent can position itself for the future.

Think about how the internet affects your life on a daily basis since it was discovered in early 1990. Internet, a system architecture that has revolutionized communications and methods of commerce by allowing various computer networks around the world to interconnect. Sometimes referred to as a network of networks, the Internet emerged in the United States in the 1970s but did not become visible to the general public until the early 1990s.

By 2020, approximately 4.5 billion people, or more than half of the world’s population, were estimated to have access to the Internet.

The Evolution of the Web

The evolution of the web can be classified into three separate stages: Web 1.0, Web 2.0, and Web 3.0.

Web 1.0  are static web sites and personal sites, the term used for the earliest version of the Internet as it emerged from its origins with Defense Advanced Research Projects Agency (DARPA) and became, for the first time, a global network representing the future of digital communications. Web 1.0  offered little information and was accessible to users across the world; these pages had little or no functionality, flexibility, or user-generated content.

Web 2.0 is called the “read/write” web, which seems to indicate an updated version of the current World Wide Web, which is known as Web 1.0. It’s more accurate to think of Web 2.0 as a shift in thinking and focus on web design. Instead of static HTML pages with little or no interaction between users, Web 2.0 represents a shift to interactive functionality and compatibility through some of the following features: User-generated content, Transparency in data and integrations.

Web 3.0 (…Loading)

Web 3.0 is the next stage of the web evolution that would make the internet more intelligent or process information with near-human-like intelligence through the power of AI systems that could run smart programs to assist users.

Tim Berners-Lee had said that the Semantic Web is meant to “automatically” interface with systems, people and home devices. As such, content creation and decision-making processes will involve both humans and machines. This would enable the intelligent creation and distribution of highly-tailored content straight to every internet consumer.

Key Features of Web 3.0

To really understand the next stage of the internet, we need to take a look at the four key features of Web 3.0:

Semantic Web

Semantic(s) is the study of the relationship between words. Therefore, the Semantic Web, according to Berners-Lee, enables computers to analyze loads of data from the Web, which includes content, transactions and links between persons.

Artificial Intelligence

Web 3.0 machines can read and decipher the meaning and emotions conveyed by a set of data, it brings forth intelligent machines. Although Web 2.0 presents similar capabilities, it is still predominantly human-based, which opens up room for corrupt behaviors such as biased product reviews, rigged ratings, etc.

For instance, online review platforms like Trustpilot provide a way for consumers to review any product or service. Unfortunately, a company can simply gather a large group of people and pay them to create positive reviews for its undeserving products. Therefore, the internet needs AI to learn how to distinguish the genuine from the fake in order to provide reliable data.

Web3.0 future for Africa

Across the world, the new Web3 economy is giving birth to myriad opportunities and the implications for the African continent are massive. Code 247 Foundation is on a mission to raised the next generation of Africa talent who will leverage the latest blockchain technologies to provide real value to billions of unbanked, underbanked and underserved individuals across Africa and other emerging markets, and we’re excited to see various blockchain protocols, startups, investors, grant funders and governments interested in doing the same.

Web3 can open up an intra-African exchange economy, it can be used for purchases and transportation between African nations. It will assist Africans to generate more economic value in a wider market.

In Africa, the evolution of blockchain technology has interested many governments across the Africa countries  to explore blockchain-based solutions, creating Central Bank Digital Currencies (CBDCs) that are likely to develop a more informed approach to the Web3 economy along with policy frameworks in line with the needs of everyday users.

Web 3 can be used to solve some of the challenges in Africa, issues of land ownership:

It is no secret the messy land management in most African countries has made it harder for citizens to acquire genuine land. This has meant that most communities are left poor due to lack of access to manage and develop their lands. Other challenges include faulk drugs, financial transactions and management of traffic etc.

Conclusion

We believe in Africa 100%. Africa can be great, will be great and must be great. Blockchain and Web3 technologies will be revolutionary in Africa. There are a lot of problems with currency and corruption in Africa.

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AI Tech

Driving intelligence solution for the automotive industry

Written by : Vivek Gouda  on Digilah (Tech Thought Leadership)

The automotive industry is rapidly adapting to the demands of connected mobility. The rise of autonomous and electric vehicles will create new challenges for manufacturers, who must implement solutions that will help them meet changing consumer needs. These vehicles are expected to require more computing power than traditional cars, which leads us to ask: What does this mean for aftermarket solutions?

What does this mean for aftermarket solutions?

As both traditional and autonomous cars become more automated, and more intelligent, the use of geospatial technology is proliferating.Geospatial intelligence (GEOINT) is the use of data and technology to improve the way we make decisions. It’s a key component of connected mobility, which refers to how vehicles communicate with each other or with infrastructure.

In autonomous vehicles, geospatial intelligence can be used to collect real-time information about road conditions as well as traffic patterns—which can help a vehicle avoid hazards that could otherwise cause an accident or delay. This type of information can also be useful for collecting data on weather conditions, or even hazards like ice on the roads during winter months.

Connected cars are another place where geospatial intelligence is being applied: they collect both driver behavior data and location information via onboard sensors that provide insights into driver quality control and safety measures such as speeding or harsh braking incidents.

But what exactly does that mean for the future of cars?

Geospatial intelligence (GEOINT) is a broad term that refers to information gathered from satellite data and other sources in order to identify people, places, and objects.

Using GEOINT, we can determine the location of a person or object within a specific area. This allows us to collect data on the location of vehicles on roads at any given time—information which is then used by car manufacturers and other companies to improve their products and services. For example, knowing where cars are parked may help you find your way into an underground parking lot before you run out of battery power in your electric vehicle; it can also be used by municipalities when designing new roads so they can plan how many lanes will be needed for traffic flow.

And how does it work?

Driving intelligence solutions allow manufacturers and OEMs to identify and engage with their customers based on their driving behavior. The solution is designed to be used by the driver, who can also access it from an app on their phone or tablet. Using this technology, car manufacturers can:

  • Monitor vehicle location & speed
  • Identify where drivers spend most of their time in the vehicle
  • Collect data on when they start and stop using the car, how long they use it for and where they go during those times

What does this mean for traditional vehicles?

Geospatial intelligence is a software solution that integrates data from multiple sources to help personnel make better decisions. In the automotive industry, it has been applied to several areas, including navigation and fleet management.

In this article, we’ll explore how geospatial intelligence can improve driver safety and efficiency in traditional vehicles.

How does it all come together?

Here’s how it all comes together:

  • Data from connected vehicles – This is the raw data collected by autonomous vehicles and other vehicle systems. It offers an on-demand picture of traffic patterns, road conditions and driver behavior.
  • Data from the cloud – The cloud allows you to store and analyze large amounts of data in real time. In this way, you can quickly identify patterns that indicate a problem with one or more sensors or systems on your vehicle.
  • Data from the edge – Edge computing uses advanced analytics at the edge of a network (a local area) rather than in a centralized location such as a cloud server center or data hub. This approach enables faster decision making because only relevant information is sent over high-bandwidth networks instead of sending all available information for analysis in another location—a process that can take hours or even days depending on bandwidth capacity limitations

Harnessing the power of geospatial intelligence will help you create better experiences for every aspect of your customer journey.

Geospatial intelligence is a powerful tool that can help you create a more personalized and engaging experience for your customers.

Heliware’s HeliAI uses location data to give you insights into how people are moving around the world, what they are doing at any given time and whether there are opportunities to engage with them at specific locations. Automotive service providers or manufacturers can use this information to understand customer behavior and improve the experiences your products offer.

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