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The rising importance of Fintech innovation in the new age

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The rising importance of Fintech innovation in the new age

The rise of fintech has opened an array of opportunities for smart cities to develop and thrive. Its importance has actually increased in the age of the pandemic that calls for social distancing or contactless transactions.

The leading global payment solutions provider Visa recently indicated the increasing role of digital payments. Thanks to the expanding role of fintech, digital payments are expected to enter different smart city sectors.

Reportedly, fintech application is going to be instrumental in the transportation sector. It will come to people in different forms of contactless payments. It will also ease the process of paying for parking or hiring bikes and scooters.

More than that, whether it’s about loans, money transfer, investment, accounting and bookkeeping, airtime or fundraising. Smart cities and businesses are going to hugely rely on fintech in the coming future. 

Going ahead, we are delving into understanding the fintech situation in three smart cities. All three are important fintech hubs that the entire world looks upon.

London

In the smart city culture, London has the reputation of being the ‘fintech capital’ of the world. The number of fintech giants in the city is valued at more than $1 billion.

However, the pandemic has caused a number of businesses to shut down. At the same time, it has also catalysed the shift to digital and contactless. Businesses are now adopting new ways to support their customers.

Even in this time of crisis, London is at the foremost position of producing the next generation of fintech leaders. This is as per the Ed Lane, VP of Sales for the EMEA region at nCino, a US-based cloud banking provider. 

Remote work is becoming a necessity due to COVID-19. Hence, investments in different technologies and solutions in financial organisations and service providers are “more important than ever”. And so Lane claims that this has increased the adoption of cloud-based banking software developed by his firm. 

The UK recently introduced the Bounce Back Loan Scheme and the Coronavirus Business Interruption Loan Scheme (CBILS). This is helping Lane’s company nCino and others. They are offering a Bank Operating System to aid SMEs with effective processing of loan applications. 

Fintech companies are surviving and tapping into benefits in the COVID-19 age due to their disruptive mindset. The dot.com crash of 2001 and the financial crash of 2008 are drivers that lead them to become proactive.

Innovatively, fintech companies started offering mobile banking, online money management tools and other personalised solutions. Today, the same is enabling them to prevail during this pandemic. Besides all, partnerships have proven to be key strategies in achieving even the impossible, as experts say. 

Singapore

Singapore is showcasing a pioneering move in the fintech industry. Fintech is at the core of Singapore’s vision to become a ‘Smart Nation’ with a “Smart Financial Centre.”

To achieve the dream, the city-state has been showing constant efforts by using innovative technology. With this, it intends to pave the way for new opportunities, enhance efficiency and improve national management of financial risks.

Until 2019, Singapore was already home to over 600 fintech firms. These companies attracted more than half of the total funding for the same year. And amidst the COVID-19 pandemic, the Monetary Authority of Singapore (MAS) introduced two major support packages.

First on April 8, 2020, it announced a S$125 million COVID-19 care package for the financial and fintech sectors. This package aims at aiding the sectors in fighting the challenges from the COVID-19 health crisis. It will help in supporting workers, accelerate digitalisation, and improve operational readiness and resilience. 

Second, on May 13, 2020, MAS, the Singapore Fintech Association (SFA) and AMTD Foundation launched the MAS-SFA-AMTD Fintech Solidarity Grant. The S$6 million grant proposes to support Singapore-based fintech firms.

A specific focus is on managing cash flow, producing new sales and seeking growth strategies. At the individual level, many industry participants have launched their own initiatives to support the sector.

Hong Kong

HongKong’s fintech startup sector tells us a different story which involves the role of blockchain. Blockchain-based companies are dominating the city’s startup sector.

In 2019, enterprise DLT and crypto-assets exchanges earned rankings as the most popular sectors in Hong Kong’s fintech industry. The report comes from the Financial Services and Treasury Bureau. It confirms that blockchain startups make up 40% of the 57 Fintech firms established in the city in 2019.

As per reports, 45% of new companies are focused on developing applications for large businesses. This is the reason that enterprise blockchain firms were the most popular. Another 27% account for blockchain-related firms in Hong Kong involved in digital currency.  

The increase in the number of blockchain-based fintech startups is due to the Special Administrative Region of the People’s Republic of China. The authority introduced new policies towards blockchain tech development – making it a priority.

Blockchain is thriving in Hong Kong due to a number of reasons. The city has laid down clear regulatory guidelines for blockchain-related businesses. Many have leveraged the benefits of the QMAS program. It enables applicants to settle down in the region before having to look for employment. This has immensely encouraged several blockchain specialists to move to Hong Kong.

The city government is also entering partnerships to expand its fintech footprint in the right direction. For example, in November 2019, the government collaborated with Thailand’s officials to explore the development of Central Bank Digital Currencies (CBDCs). Blockchain is a promising technology for the fintech industry. It supports quick, secure and cost-effective transaction-related services.

More importantly, it provides transparency that other traditional technologies were not capable of. Thanks to the use of encrypted distributed ledgers. These enable real-time verification of transactions without the need for mediators such as correspondent banks.

Why Is Fintech Innovation Important For The Development Of Smart Cities?

Fintech Boosting Business And Growth Opportunities In Smart Cities

Advanced cities that are now smart cities have been using fintech for their development. With that, they are also leading the way for others to follow. Many experts confirm that innovation in fintech is a must for any city to become a ‘smart city.’

It enables easy national as well as international business. For the residents, it makes life more convenient by encouraging contactless, economical, sustainable and efficient payment-related operations. 

One important aspect that smart city development and fintech innovation has in common is their determination to cut bureaucracy. A city that manages to enable speedy and inexpensive international transfers will also enable its citizens with greater access to the global market. This is as said by Hans W. Winterhoff from KPMG in one of his articles.

Furthermore, fintech innovations of the past have demonstrated their success. Some fintech applications have simplified procedures that became unnecessarily complex over time. Traditional banking services are one of the biggest examples. 

The innovative fintech services opened doors for online shopping and easy international money transfers. Fintech is able to provide the same product or service to consumers. But that’s happening in less time, with fewer steps, and at more affordable rates.

Besides, transparency is another important factor that is allowing consumers to have faith in fintech services. With the current potential of fintech, we can now say that it is one of the essential pillars of successful smart city development. The results are already here in the age of this pandemic.

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Source: https://www.fintechnews.org/the-rising-importance-of-fintech-innovation-in-the-new-age-2/

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What Waabi’s launch means for the self-driving car industry

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Elevate your enterprise data technology and strategy at Transform 2021.


It is not the best of times for self-driving car startups. The past year has seen large tech companies acquire startups that were running out of cash and ride-hailing companies shutter costly self-driving car projects with no prospect of becoming production-ready anytime soon.

Yet, in the midst of this downturn, Waabi, a Toronto-based self-driving car startup, has just come out of stealth with an insane amount of $83.5 million in a Series A funding round led by Khosla Ventures, with additional participation from Uber, 8VC, Radical Ventures, OMERS Ventures, BDC, and Aurora Innovation. The company’s financial backers also include Geoffrey Hinton, Fei-Fei Li, Peter Abbeel, and Sanja Fidler, artificial intelligence scientists with great influence in the academia and applied AI community.

What makes Waabi qualified for such support? According to the company’s press release, Waabi aims to solve the “scale” challenge of self-driving car research and “bring commercially viable self-driving technology to society.” Those are two key challenges of the self-driving car industry and are mentioned numerous times in the release.

What Waabi describes as its “next generation of self-driving technology” has yet to pass the test of time. But its execution plan provides hints at what directions the self-driving car industry could be headed.

Better machine learning algorithms and simulations

According to Waabi’s press release: “The traditional approach to engineering self-driving vehicles results in a software stack that does not take full advantage of the power of AI, and that requires complex and time-consuming manual tuning. This makes scaling costly and technically challenging, especially when it comes to solving for less frequent and more unpredictable driving scenarios.”

Leading self-driving car companies have driven their cars on real roads for millions of miles to train their deep learning models. Real-road training is costly both in terms of logistics and human resources. It is also fraught with legal challenges as the laws surrounding self-driving car tests vary in different jurisdictions. Yet despite all the training, self-driving car technology struggles to handle corner cases, rare situations that are not included in the training data. These mounting challenges speak to the limits of current self-driving car technology.

Here’s how Waabi claims to solve these challenges (emphasis mine): “The company’s breakthrough, AI-first approach, developed by a team of world leading technologists, leverages deep learning, probabilistic inference and complex optimization to create software that is end-to-end trainable, interpretable and capable of very complex reasoning. This, together with a revolutionary closed loop simulator that has an unprecedented level of fidelity, enables testing at scale of both common driving scenarios and safety-critical edge cases. This approach significantly reduces the need to drive testing miles in the real world and results in a safer, more affordable, solution.”

There’s a lot of jargon in there (a lot of which is probably marketing lingo) that needs to be clarified. I reached out to Waabi for more details and will update this post if I hear back from them.

By “AI-first approach,” I suppose they mean that they will put more emphasis on creating better machine learning models and less on complementary technology such as lidars, radars, and mapping data. The benefit of having a software-heavy stack is the very low costs of updating the technology. And there will be a lot of updating in the coming years as scientists continue to find ways to circumvent the limits of self-driving AI.

The combination of “deep learning, probabilistic reasoning, and complex optimization” is interesting, albeit not a breakthrough. Most deep learning systems use non-probabilistic inference. They provide an output, say a category or a predicted value, without giving the level of uncertainty on the result. Probabilistic deep learning, on the other hand, also provides the reliability of its inferences, which can be very useful in critical applications such as driving.

“End-to-end trainable” machine learning models require no manual-engineered features. This means once you have developed the architecture and determined the loss and optimization functions, all you need to do is provide the machine learning model with training examples. Most deep learning models are end-to-end trainable. Some of the more complicated architectures require a combination of hand-engineered features and knowledge along with trainable components.

Finally, “interpretability” and “reasoning” are two of the key challenges of deep learning. Deep neural networks are composed of millions and billions of parameters. This makes it hard to troubleshoot them when something goes wrong (or find problems before something bad happens), which can be a real challenge in critical scenarios such as driving cars. On the other hand, the lack of reasoning power and causal understanding makes it very difficult for deep learning models to handle situations they haven’t seen before.

According to TechCrunch’s coverage of Waabi’s launch, Raquel Urtasan, the company’s CEO, described the AI system the company uses as a “family of algorithms.”

“When combined, the developer can trace back the decision process of the AI system and incorporate prior knowledge so they don’t have to teach the AI system everything from scratch,” TechCrunch wrote.

self-driving car simulation carla

Above: Simulation is an important component of training deep learning models for self-driving cars. (credit: CARLA)

Image Credit: Frontier Developments

The closed-loop simulation environment is a replacement for sending real cars on real roads. In an interview with The Verge, Urtasan said that Waabi can “test the entire system” in simulation. “We can train an entire system to learn in simulation, and we can produce the simulations with an incredible level of fidelity, such that we can really correlate what happens in simulation with what is happening in the real world.”

I’m a bit on the fence on the simulation component. Most self-driving car companies are using simulations as part of the training regime of their deep learning models. But creating simulation environments that are exact replications of the real world is virtually impossible, which is why self-driving car companies continue to use heavy road testing.

Waymo has at least 20 billion miles of simulated driving to go with its 20 million miles of real-road testing, which is a record in the industry. And I’m not sure how a startup with $83.5 million in funding can outmatch the talent, data, compute, and financial resources of a self-driving company with more than a decade of history and the backing of Alphabet, one of the wealthiest companies in the world.

More hints of the system can be found in the work that Urtasan, who is also a professor in the Department of Computer Science at the University of Toronto, does in academic research. Urtasan’s name appears on many papers about autonomous driving. But one in particular, uploaded on the arXiv preprint server in January, is interesting.

Titled “MP3: A Unified Model to Map, Perceive, Predict and Plan,” the paper discusses an approach to self-driving that is very close to the description in Waabi’s launch press release.

MP3 self-driving neural networks probablistic deep learning

Above: MP3 is a deep learning model that uses probabilistic inference to create scenic representations and perform motion planning for self-driving cars.

The researchers describe MP3 as “an end-to-end approach to mapless driving that is interpretable, does not incur any information loss, and reasons about uncertainty in the intermediate representations.” In the paper researchers also discuss the use of “probabilistic spatial layers to model the static and dynamic parts of the environment.”

MP3 is end-to-end trainable and uses lidar input to create scene representations, predict future states, and plan trajectories. The machine learning model obviates the need for finely detailed mapping data that companies like Waymo use in their self-driving vehicles.

Raquel posted a video on her YouTube that provides a brief explanation of how MP3 works. It’s fascinating work, though many researchers will point out that it not so much of a breakthrough as a clever combination of existing techniques.

There’s also a sizeable gap between academic AI research and applied AI. It remains to be seen if MP3 or a variation of it is the model that Waabi is using and how it will perform in practical settings.

A more conservative approach to commercialization

Waabi’s first application will not be passenger cars that you can order with your Lyft or Uber app.

“The team will initially focus on deploying Waabi’s software in logistics, specifically long-haul trucking, an industry where self-driving technology stands to make the biggest and swiftest impact due to a chronic driver shortage and pervasive safety issues,” Waabi’s press release states.

What the release doesn’t mention, however, is that highway settings are an easier problem to solve because they are much more predictable than urban areas. This makes them less prone to edge cases (such as a pedestrian running in front of the car) and easier to simulate. Self-driving trucks can transport cargo between cities, while human drivers take care of delivery inside cities.

With Lyft and Uber failing to launch their own robo-taxi services, and with Waymo still away from turning One, its fully driverless ride-hailing service, into a scalable and profitable business, Waabi’s approach seems to be well thought.

With more complex applications still being beyond reach, we can expect self-driving technology to make inroads into more specialized settings such as trucking and industrial complexes and factories.

Waabi also doesn’t make any mention of a timeline in the press release. This also seems to reflect the failures of the self-driving car industry in the past few years. Top executives of automotive and self-driving car companies have constantly made bold statements and given deadlines about the delivery of fully driverless technology. None of those deadlines have been met.

Whether Waabi becomes independently successful or ends up joining the acquisition portfolio of one of the tech giants, its plan seems to be a reality check on the self-driving car industry. The industry needs companies that can develop and test new technologies without much fanfare, embrace change as they learn from their mistakes, make incremental improvements, and save their cash for a long race.

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics.

This story originally appeared on Bdtechtalks.com. Copyright 2021

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Source: https://venturebeat.com/2021/06/12/what-waabis-launch-means-for-the-self-driving-car-industry/

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10 steps to educate your company on AI fairness

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Elevate your enterprise data technology and strategy at Transform 2021.


As companies increasingly apply artificial intelligence, they must address concerns about trust.

Here are 10 practical interventions for companies to employ to ensure AI fairness. They include creating an AI fairness charter and implementing training and testing.

Data-driven technologies and artificial intelligence (AI) are powering our world today — from predicting where the next COVID-19 variant will arise, to helping us travel on the most efficient route. In many domains, the general public has a high amount of trust that the algorithms that are powering these experiences are being developed in a fair manner.

However, this trust can be easily broken. For example, consider recruiting software that, due to unrepresentative training data, penalizes applications that contain the word “women”, or a credit-scoring system that misses real-world evidence of credit-worthiness and thus as a result certain groups get lower credit limits or are denied loans.

The reality is that the technology is moving faster than the education and training on AI fairness. The people who train, develop, implement and market these data-driven experiences are often unaware of the second or third-order implications of their hard work.

As part of the World Economic Forum’s Global Future Council on Artificial Intelligence for Humanity, a collective of AI practitioners, researchers and corporate advisors, we propose 10 practical interventions for companies to employ to ensure AI fairness.

1. Assign responsibility for AI education

Assign a chief AI ethics officer (CAIO) who along with a cross-functional ethics board (including representatives from data science, regulatory, public relations, communications and HR) should be responsible for the designing and implementing AI education activities. The CAIO should also be the “ombudsman” for staff to reach out to in case of fairness concerns, as well as a spokesperson to non-technical staff. Ideally this role should report directly to the CEO for visibility and implementation.

2. Define fairness for your organization

Develop an AI fairness charter template and then ask all departments that are actively using AI to complete it in their context. This is particularly relevant for business line managers and product and service owners.

3. Ensure AI fairness along the supply chain

Require suppliers you are using who have AI built into their procured products and services – for instance a recruiting agency who might use AI for candidate screening – to also complete an AI fairness charter and to adhere to company policies on AI fairness. This is particularly relevant for the procurement function and for suppliers.

4. Educate staff and stakeholders through training and a “learn by doing” approach

Require mandatory training and certification for all employees on AI fairness principles – similar to how staff are required to sign up to codes of business conduct. For technical staff, provide training on how to build models that do not violate fairness principles. All trainings should leverage the insights from the AI fairness charters to directly address issues facing the company. Ensure the course content is regularly reviewed by the ethics board.

5. Create an HR AI fairness people plan

An HR AI fairness plan should include a yearly review by HR to assess the diversity of the team working on data-driven technologies and AI, and an explicit review and upgrade of the competencies and skills that are currently advertised for key AI-relevant product development roles (such as product owner, data scientist and data engineer) to ensure awareness of fairness is part of the job description.

6. Test AI fairness before any tech launches

Require departments and suppliers to run and internally publish fairness outcomes tests before any AI algorithm is allowed to go live. Once you know what groups may be unfairly treated due to data bias, simulate users from that group and monitor the results. This can be used by product teams to iterate and improve their product or service before it goes live. Open source tools, such as Microsoft Fairlearn, can help provide the analysis for a fairness outcome test.

7. Communicate your approach to AI fairness

Set up fairness outcomes learning sessions with customer- and public-facing staff to go through the fairness outcomes tests for any new or updated product or service. This is particularly relevant for marketing and external communications, as well as customer service teams.

8. Dedicate a standing item in board meetings to AI fairness processes

This discussion should include the reporting on progress and adherence, themes raised from the chief AI ethics officer and ethics board, and the results of high-priority fairness outcomes tests

9. Make sure the education sticks

Regularly track and report participation and completion of the AI fairness activities, along with the demonstrated impact of managing fairness in terms of real business value. Provide these updates to department and line managers to communicate to staff to reinforce that by making AI platforms and software more fair, the organization is more effective and productive.

10. Document everything

Document your approach to AI fairness and communicate it in staff and supplier trainings and high-profile events, including for customers and investors.

[This story originally appeared on 10 steps to educate your company on AI fairness | World Economic Forum (weforum.org). Copyright 2021.]

Nadjia Yousif is Managing Director and Partner at Boston Consulting Group and co-leads the Financial Institutions practice for the UK the Netherlands and Belgium.

Mark Minevich is Chair for Artificial Intelligence Policy at the International Research Centre on Artificial Intelligence under the auspices of UNESCO, Jozef Stefan Institute.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

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Source: https://venturebeat.com/2021/06/11/10-steps-to-educate-your-company-on-ai-fairness/

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The rise of robotaxis in China

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AutoX, Momenta and WeRide took the stage at TC Sessions: Mobility 2021 to discuss the state of robotaxi startups in China and their relationships with local governments in the country.

They also talked about overseas expansion — a common trajectory for China’s top autonomous vehicle startups — and shed light on the challenges and opportunities for foreign AV companies eyeing the massive Chinese market.


Enterprising governments

Worldwide, regulations play a great role in the development of autonomous vehicles. In China, policymaking for autonomous driving is driven from the bottom up rather than a top-down effort by the central government, observed executives from the three Chinese robotaxi startups.

Huan Sun, Europe general manager at Momenta, which is backed by the government of Suzhou, a city near Shanghai, said her company had a “very good experience” working with the municipal governments across multiple cities.

In China, each local government is incentivized to really act like entrepreneurs like us. They are very progressive in developing the local economy… What we feel is that autonomous driving technology can greatly improve and upgrade the [local governments’] economic structure. (Time stamp: 02:56)

Shenzhen, a special economic zone with considerable lawmaking autonomy, is just as progressive in propelling autonomous driving forward, said Jewel Li, chief operation officer at AutoX, which is based in the southern city.

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Source: https://techcrunch.com/2021/06/11/the-rise-of-robotaxis-in-china/

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