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Artificial Intelligence

Croatia’s Gideon Brothers raises $31M for its 3D vision-enabled autonomous warehouse robots

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Proving that Central and Eastern Europe remains a powerhouse of hardware engineering matched with software, Gideon Brothers (GB), a Zagreb, Croatia-based robotics and AI startup, has raised a $31 million Series A round led by Koch Disruptive Technologies (KDT), the venture and growth arm of Koch Industries Inc., with participation from DB Schenker, Prologis Ventures and Rite-Hite.

The round also includes participation from several of Gideon Brothers’ existing backers: Taavet Hinrikus (co-founder of TransferWise), Pentland Ventures, Peaksjah, HCVC (Hardware Club), Ivan Topčić, Nenad Bakić and Luca Ascani.

The investment will be used to accelerate the development and commercialization of GB’s AI and 3D vision-based “autonomous mobile robots” or “AMRs”. These perform simple tasks such as transporting, picking up and dropping off products in order to free up humans to perform more valuable tasks.

The company will also expand its operations in the EU and U.S. by opening offices in Munich, Germany and Boston, Massachusetts, respectively.

Gideon Brothers founders

Gideon Brothers founders. Image Credits: Gideon Brothers

Gideon Brothers make robots and the accompanying software platform that specializes in horizontal and vertical handling processes for logistics, warehousing, manufacturing and retail businesses. For obvious reasons, the need to roboticize supply chains has exploded during the pandemic.

Matija Kopić, CEO of Gideon Brothers, said: “The pandemic has greatly accelerated the adoption of smart automation, and we are ready to meet the unprecedented market demand. The best way to do it is by marrying our proprietary solutions with the largest, most demanding customers out there. Our strategic partners have real challenges that our robots are already solving, and, with us, they’re seizing the incredible opportunity right now to effect robotic-powered change to some of the world’s most innovative organizations.”

He added: “Partnering with these forward-thinking industry leaders will help us expand our global footprint, but we will always stay true to our Croatian roots. That is our superpower. The Croatian startup scene is growing exponentially and we want to unlock further opportunities for our country to become a robotics & AI powerhouse.”

Annant Patel, director at Koch Disruptive Technologies, said: “With more than 300 Koch operations and production units globally, KDT recognizes the unique capabilities of and potential for Gideon Brothers’ technology to substantially transform how businesses can approach warehouse and manufacturing processes through cutting edge AI and 3D AMR technology.”

Xavier Garijo, member of the Board of Management for Contract Logistics, DB Schenker, added: “Our partnership with Gideon Brothers secures our access to best in class robotics and intelligent material handling solutions to serve our customers in the most efficient way.”

GB’s competitors include Seegrid, Teradyne (MiR), Vecna Robotics, Fetch Robotics, AutoGuide Mobile Robots, Geek+ and Otto Motors.

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Source: https://techcrunch.com/2021/06/08/croatias-gideon-brothers-raises-31m-for-its-%e2%80%a83d-vision-enabled-autonomous-warehouse-robots/

Artificial Intelligence

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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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.

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

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Artificial Intelligence

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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Can we afford AI?

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Elevate your enterprise data technology and strategy at Transform 2021.


Of all the concerns surrounding artificial intelligence these days — and no, I don’t mean evil robot overlords, but more mundane things like job replacement and security — perhaps none is more overlooked than cost.

This is understandable, considering AI has the potential to lower the cost of doing business in so many ways. But AI is not only expensive to acquire and deploy, it also requires a substantial amount of compute power, storage, and energy to produce worthwhile returns.

Back in 2019, AI pioneer Elliot Turner estimated that training the XLNet natural language system could cost upwards of $245,000 – roughly 512 TPUs running at full capacity for 60 straight hours. And there is no guarantee it will produce usable results. Even a simple task like training an intelligent machine to solve a Rubik’s Cube could draw up to 2.8GW of power, about the hourly output of three nuclear power plants. These are serious — although still debatable — numbers, considering that some estimates claim technology processes will draw more than half of our global energy output by 2030.

Silicon solution

Perhaps no one understands this better than IBM, which has been at the forefront of the AI evolution — with varying degrees of success –thanks to platforms like Watson and Project Debater. The company’s Albany, New York-based research lab has an AI Hardware Center that might be on the verge of unveiling some intriguing results in the drive to reduce the computational demands of training AI and guiding its decision-making processes, according to Tirias Research analyst Kevin Krewell.

A key development is a quad-core test chip recently unveiled at the International Solid-State Circuits Conference (ISSCC). The chip features a hybrid 8-bit floating-point format for training functions and both 2- and 4-bit integer formats for inference, Krewell wrote in a Forbes piece. This would be a significant improvement over the 32-bit floating-point solutions that power current AI solutions, but only if the right software can be developed to produce the same or better results under these lower logic and memory footprints. So far, IBM has been silent on how it intends to do this, although the company has announced that its DEEPTOOLS compiler, which supports AI model development and training, is compatible with the 7nm die.

Qualcomm is also interested in driving greater efficiency in AI models, with a particular focus on Neural Architecture Search (NAS), the means by which intelligent machines map the most efficient network topologies to accomplish a given task. But since Qualcomm’s chips generally have a low power footprint to begin with, its focus is on developing new, more efficient models that work comfortably within existing architectures, even at scale.

All for one

To that end, the company says it has adopted a holistic approach to modeling that stresses the need to shrink multiple axes — like quantization, compression, and compilation — in a coordinated fashion. Since all of these techniques complement each other, researchers must address the efficiency challenge from their unique angle but not so that a change in one area disrupts gains in another.

When applied to NAS, the key challenges are reducing high compute costs, improving scalability, and delivering more accurate hardware performance metrics. Called DONNA (Distilling Optimal Neural Network Architectures), the solution provides a highly scalable means to define network architectures around accuracy, latency, and other requirements and then deploy them in real-world environments. The company is already reporting a 20% speed boost over MobileNetV2 in locating highly accurate architectures on a Samsung S21 smartphone.

Facebook also has a strong interest in fostering greater efficiency in AI. The company recently unveiled a new algorithm called Seer (SElf-supERvised) that reduces the amount of labeling required to make effective use of datasets. The process allows AI to draw accurate conclusions using a smaller set of comparative data. In this way, it can identify, say, a picture of a cat without having to comb through thousands of existing pictures that have already been labeled as cats. This reduces the number of human hours required in training, as well as the overall data footprint required for identification, all of which speeds up the process and lowers overall costs.

Speed, efficiency, and reduced resource consumption have been driving factors in IT for decades, so it’s no surprise that these goals are starting to drive AI development as well. What is surprising is the speed at which this is happening. Traditionally, new technologies are deployed first, leaving things like costs and efficiency as afterthoughts.

It’s a sign of the times that AI is already adopting streamlined architectures and operations as core capabilities before it hits a critical level of scale. Even the most well-heeled companies recognize that the computational requirements of AI are likely to be far greater than anything they’ve encountered before.

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Source: https://venturebeat.com/2021/06/11/can-we-afford-ai/

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