Monthly Archives: September 2021

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Technology, and especially artificial intelligence (AI), is changing the way business gets done. In virtually every market worldwide, automation, business intelligence, and new systems are synthesizing operations, enabling teams to work smarter. While it may feel that new tech is being plugged in at an impressive pace, there are some late adopters, one of which is vendor relationship management.

Third-party vendors are essential for companies of any size. Manufacturers, suppliers and service providers are used for everything from product development to cybersecurity. At an enterprise level, the number of vendors can reach the tens of thousands. As is often the case in systems that have been around for a decade or more, interoperability is virtually non-existent, data is hard to find and efficiency is a pipe dream.

Sourcing and vendor management are more complex than ever, as companies move into emerging fields and leverage third-party suppliers to provide what cannot be internally produced. Vendors play long-term and essential roles in businesses. Many procurement departments have long held to an us versus them mentality, but as the number of necessary suppliers grows, so does the need for collaboration.

The type of vendor management in most business contexts today is sufficient for managing a handful of vendors with some success. But the demands of this are changing, and in larger companies, an entire vendor ecosystem requires more sophisticated management. In this context, the sheer volume of contacts, contracts, products, tasks and more far exceeds existing practices. Fortunately, data is what AI handles best, and new technology is being developed to get vendor relationship management up to speed.

CIOs, chief procurement leaders and financial officers regularly report on how difficult it is to understand the vendor relationship from a contract perspective, to understand how much tech a company actually owned versus used, to keep track of all of the support personnel and even to find the vendor data that lived in their ecosystem. Most of this has been and is still managed through email or on spreadsheets.

For years, client and customer communication have been managed via client relationship management (CRM) software, while vendor relationships have been left to outmoded methods. Solutions have been patchwork, piecemeal and largely ineffective. Even at the highest enterprise level, in companies with extensive technology, a problem persists: it is difficult to manage vendor relationships and assets in one place.

Brandon Card

A first reason is that there are disparate data sets, and enterprise businesses are notoriously siloed. Vendor information may reside in the legal department, the IT department and be tied to product records. A second reason this challenge has not been solved sooner is that every company hires vendors on different terms, and often each vendor on different terms. The compound challenges of these two issues alone have kept companies from making changes, but now that AI is more accessible, a smart solution is possible.

Industry leader Brandon Card is a well-known expert who has worked in development and sales for many companies, including IBM and Microsoft. He launched an application called Terzo, the first Vendor Relationship Management (VRM) system. The idea is to create a platform where vendors can now be managed in the way contacts have been managed by marketers.

CRMs already pull data from different sources, integrate with existing systems, have visibility across departments and centralize and visualize data in a meaningful way. CRMs have a long history of success in managing contexts, and the data obtained in them can trigger alerts, renewal notices and even alert marketers to churn risk. No successful company manages client relationships in any other way, and AI has already amplified the capacity of CRMs, making it easier to filter, sort and interpret data.

The same technology is at work in the emerging area of VRMs. Vendor management can be handled as data from various sources is added to the system, including contract dates, contract details, vendor supplier contact information, vendor communication records and more. Any procurement department in any company will attest to how transformational this process will be, alleviating the manual burden of record-keeping and retrieval, and leveraging the power of AI to manage these crucial relationships more effectively.

Enterprise resource planning (ERP) systems automate business processes, and are often the primary way businesses manage day-to-day operations. While these systems accomplish their purpose, they continue to be an inadequate way to manage vendors. Card explains, Across all of the systems in place today, customers rely upon ERP systems to manage all of their suppliers' data. ERP systems are a black box and cant solve vendor challenges for today, which is why people resort to spreadsheets. The problem is that, ten years ago, these companies were doing everything in-house with their own data centers.

As company offerings get more niched, and products become more technically complex, in-house solutions will not suffice. Businesses have grown and strategic vendors deliver cost-saving solutions. In most companies, entire departments cant exist without third-party vendors. AI provides the first glimmer of hope for a digital platform capable of helping businesses manage all of vendor relationships and the stakeholders that manage those relationships. These transactional relationships have now become critical partnerships, and because of AI, the technology has finally caught up to the need.

Vendor relationships directly impact a companys success. These critical partnerships require collaboration, and a vendor hub built on AI may provide that solution. It is a new way of working that complements existing systems. Easily integrated with a companys tech stack, this solution enriches data and user experiences and uncovers insights faster.

Contract data provides the single source of truth, from which AI can then extract all products, services and legal terms. Companies understand what they own and how much theyre paying for it. AI speeds up time to visibility and gets insights five to six times faster.

This is a new way of treating strategic suppliers as business partners, and empowering stakeholders with tools to more effectively connect, communicate, monitor and achieve results. As technology continues to improve and upgrade business operations, solutions like these make it possible for companies to do more.

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Vendor Management Is The New Customer Management, And AI Is Transforming The Sector Already - Forbes

A big about-face: The Clearview decision represents a major win for civil society groups and researchers focused on bringing transparency and accountability to dealings between the government and the private sector. Those groups had argued that the legal mandates were a tactic to dissuade advocacy groups from further investigating the company or others like it, and could undermine press freedom by targeting journalists and their sources.

A controversial technology: Facial recognition tools have long been under scrutiny by privacy and civil rights advocates whove raised alarm about biases with the technology generally and the disproportionate harm it can cause to marginalized communities.

After 2019 research from Open The Government shed light on police use of this surveillance including Clearviews tech and later prompted reporting on the startup, the software firm now finds itself tangled in multidistrict consumer privacy litigation in Illinois.

The debate over the subpoenas: The subpoenas included demands for communications with journalists about Clearview and its leaders, as well as information theyd uncovered about the company and its founders in public records requests.

Clearview's attorney Andrew J. Lichtman said previously that the subpoenas were served as part of its defense in the litigation in Illinois. But that case does not appear to involve Open The Government or the other parties subpoenaed, and Clearview would not explain how the groups' correspondence with journalists would be in any way relevant.

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Clearview AI drops subpoenas of its critics - POLITICO

SAN JOSE, Calif.--(BUSINESS WIRE)--Sep 27, 2021--

Recently, MLCommons, a well-known open engineering consortium, released the results of MLPerf Inference V1.1, the leading AI benchmark suite. In the very competitive Closed Division, Inspur ranked first in 15 out of 30 tasks, making it the most successful vendor at the event.

Inspur Results in MLPerf TM Inference V1.1

Vendor

Division

System

Model

Accuracy

Score

Units

Inspur

Data CenterClosed

NF5688M6

3D-UNet

Offline, 99%

498.03

Samples/s

NF5688M6

3D-UNet

Offline, 99.9%

498.03

Samples/s

NF5488A5

DLRM

Offline, 99%

2607910

Samples/s

NF5688M6

DLRM

Server, 99%

2608410

Queries/s

NF5488A5

DLRM

Offline, 99.9%

2607910

Samples/s

NF5688M6

DLRM

Server, 99.9%

2608410

Queries/s

EdgeClosed

NE5260M5

3D-UNet

Offline, 99%

93.49

Samples/s

NE5260M5

3D-UNet

Offline, 99.9%

93.49

Samples/s

NE5260M5

Bert

Offline, 99%

5914.13

Samples/s

NF5688M6

Bert

SingleStream, 99%

1.54

Latency (ms)

NF5688M6

ResNet50

SingleStream, 99%

0.43

Latency (ms)

NE5260M5

RNNT

Offline, 99%

24446.9

Samples/s

NF5688M6

RNNT

SingleStream, 99%

18.5

Latency (ms)

NF5688M6

SSD-ResNet34

SingleStream, 99%

1.67

Latency (ms)

NF5488A5

SSD-MobileNet

SingleStream, 99%

0.25

Latency (ms)

Developed by Turing Award winner David Patterson and leading academic institutions, MLPerf is the leading industry benchmark for AI performance. Founded in 2020 and based on MLPerf benchmarks, MLCommons is an open non-profit engineering consortium dedicated to advancing standards and metrics for machine learning and AI performance. Inspur is a founding member of MLCommons, along with over 50 other leading organizations and companies from across the AI landscape.

In the MLPerf Inference V1.1 benchmark test, the Closed Division included two categories Data Center (16 tasks) and Edge (14 tasks). Under the Data Center category, six models were covered, including Image Classification (ResNet50), Medical Image Segmentation (3D-UNet), Object Detection (SSD-ResNet34), Speech Recognition (RNN-T), Natural Language Processing (BERT), and Recommendation (DLRM). A high accuracy mode (99.9%) was set for BERT, DLRM and 3D-UNET. Every model task evaluated the performance in both Server and Offline scenarios with the exception 3D-UNET, which was only evaluated in the Offline scenario. For the Edge category, the Recommendation (DLRM) model was removed and the Object Detection (SSD-MobileNet) model was added. A high accuracy mode (99.9%) was set for 3D-UNET. All models were tested for both Offline and Single Stream inference.

In the extremely competitive Closed Division, in which mainstream vendors were competing, the use of the same models and optimizers was required by all participants. Doing so provided the ability to easily evaluate and compare AI computing system performance among various vendors. Nineteen vendors including Nvidia, Intel, Inspur, Qualcomm, Alibaba, Dell, and HPE participated in the Closed Division. A total of 1,130 results were submitted, including 710 for the Data Center category, and 420 for the Edge category.

Full-Stack AI Capabilities Ramp up Performance

Inspur achieved excellent results in this MLPerf competition with its three AI servers NF5488A5, NF5688M6, and NE5260M5.

Inspur ranked first in 15 tasks covering all AI models, including Medical Image Recognition, Natural Language Processing, Image Classification, Speech Recognition, Recommendation, as well as Object Detection (SSD-ResNet34 and SSD-MobileNet). The results showcase that from Cloud to Edge, Inspur is ahead of the Industry in nearly all aspects. Inspur was able to make huge strides in performance in various tasks under the Data Center category compared to previous MLPerf events despite no changes to its server configuration. Its model performance results in Image Classification (ResNet50) and Speech Recognition (RNN-T) increased by 4.75% and 3.83% compared to the V1.0 competition just six months ago.

The outstanding performance of Inspur's AI servers in the MLPerf Benchmark Test can be credited to Inspur's exceptional system design and full-stack optimization in AI computing systems. Through precise calibration and optimization, CPU and GPU performance as well as the data communication between CPUs and GPUs were able to reach the highest levels for AI inference. Additionally, by enhancing the round-robin scheduling for multiple GPUs based on GPU topology, the performance of a single GPU or multiple GPUs can be increased nearly linearly.

Inspur NF5488A5 was the only AI server in this MLPerf competition to support eight 500W A100 GPUs with liquid cooling technology, which significantly boosted AI computing performance. Among mainstream high-end AI servers with 8 NVIDIA A100 SXM4 GPUs, Inspur's servers came out on top in all 16 tasks in the Closed Division under the Data Center category.

As a leading AI computing company, Inspur is committed to the R&D and innovation of AI computing, including both resource-based and algorithm platforms. It also works with other leading AI enterprises to promote the industrialization of AI and the development of AI-driven industries through its Meta-Brain technology ecosystem.

To view the complete results of MLPerf Inference v1.1, please visit:

Inspur Information is a leading provider of data center infrastructure, cloud computing, and AI solutions. It is the worlds 2 nd largest server manufacturer. Through engineering and innovation, Inspur delivers cutting-edge computing hardware design and extensive product offerings to address important technology arenas like open computing, cloud data center, AI, and deep learning. Performance-optimized and purpose-built, our world-class solutions empower customers to tackle specific workloads and real-world challenges. To learn more, please go to https://www.inspursystems.com/.

KEYWORD: UNITED STATES NORTH AMERICA CALIFORNIA

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Inspur Comes Out on Top with Superior AI Performance in MLPerf Inference V1.1 - Maryville Daily Times

Do you ever think about spatial barriers?

In 1997, a chess computer named Deep Blue became the first in the world to beat a reigning chess champion. The machine, complex and high-speed, went toe-to-toe with Garry Kasparov. The AI processed information at great speed, overcoming spatial barriers with processing power.

Now, an AI like Deep Blue is less startling to humankind than it was initially. But coevolving informatics continue to reshape the frontiers of what information can do. Associate Professor Girard examines how information can re-shape our understanding of gender equality, or evaluate the resources available to the people of Afghanistan, especially when it comes to the urban-rural divide.

When it comes back down to the kind of information a brain or AI can process, there are new ways for coevolving informatics to grow. For instance, because electromagnetic signals lack mass and travel at the speed of light, because they dont have the pathway and energy constraints of other types of information. Humans are a living testament to the power of evolved information use, as we have curated a cognitive capacity beyond that of all other living creatures. If we put those understandings together, where can we go?

According to research by Florida International University, the future could mean instant interfacing of brains with cloud-based AI enabling a kind of global access that is not limited to any individual capacity for memory. This would give an intensive advantage to groups with access to that globalised information.

But how could this future work? What kind of problems and considerations would arise?

To find out more about the imminent potential of cloud-based AI and the human brain together, read more about the work of Florida International University here.

Editor's Recommended Articles

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Coevolving informatics, AI and brain-computer interfacing - Open Access Government

HOUSTON, Sep. 27, 2021 /PRNewswire-PRWeb/ --Lone Star College-University Park today announced a new initiative that will enable faculty to cultivate more enriching, engaging class discussions utilizing artificial intelligence (AI). In partnership with the inquiry-based discussion platform Packback, LSC-University Park will expand faculty and student access to a platform shown to promote critical thinking, build a sense of community in class, and help improve student success.

"The power of a good class discussion is that it empowers students to engage with the curriculum beyond what's in their textbooksand help them to see that they're not alone in the questions they have or the observations they make," said Dr. Kathy Cecil-Sanchez, vice president of instruction at LSC-University Park. "That's the kind of critical thinking that Packback's platform helps to inspire, while also allowing faculty to foster deeper and more substantive engagement with their students."

Backed by entrepreneur Mark Cuban, Packback's unique platform assigns a "curiosity score" to students' discussion posts based on a range of factors, including the open-endedness of questions and the way that students reference sources. The tool also provides feedback on grammar, spelling, and offers students guidance on academic research processes like citing sources and paraphrasing. All these elements of the Packback platform are designed to spark students' intrinsic motivation, as well as to help instructors focus on providing more substantive guidance and support to students, rather than spending time on more repetitive tasks like correcting grammar.

"In my classes, Packback has been a platform that provides equity of access to the material while also pushing students to ensure that their analysis is based on facts," said Cassandra Khatri, LSC-University Park professor of political science. "Packback requires students cite the work they provide in their responses, which gets at the larger problem in political science of the gray area between analysis and takes. I really enjoy seeing my students practicing critical thinking skills as they deal with the tough issues of the day."

"I appreciate Packback's AI guidance on the writing and research, so I can focus more on the heart of the discussion topics that arise, added Jennifer Ross, LSC-University Park professor of political science. "Through Packback discussions I have seen students go from being passive learners who just accept what they read and hear, to actively taking an interest in current events and thinking about them critically."

The partnership comes in the wake of a recent research finding that inquiry-based discussion tools can spark deeper engagement and also boost academic performance. A study conducted by Packback in 2019-2020 with 10 higher education institutions, including LSC-University Park, found that students in classes that use Packback received more A, B, or C grades and fewer D, F and W grades than the control group. In the same study, students cited sources approximately 2.5 times as often in the treatment group versus the control group.

"Our work with colleges around the country is rooted in the belief that better approaches to discussion can help students build the skills that help them succeed both in and out of the classroom," said Kasey Gandham, co-founder of Packback. "As an institution that shares that vision, LSC-University Park has been an invaluable partner over the past few years, and we look forward to continuing to learn from its creative and student-centered approach in the years to come."

Packback's proprietary AI and machine learning technology provides inquiry-based online discussion to over 3,500 instructors and over 900,000 students, who have posted 22 million questions and responses to date.

Media Contact

Ben Watsky, Packback, +1 (203) 907-8930, watsky@whiteboardadvisors.com

SOURCE Packback

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Lone Star College-University Park Taps AI to Inspire Curiosity in Online Discussions - inForney.com

There is a need to have a safe, predictable, and stable food supply chain, Jain said. Internet of Things, artificial intelligence, and data analytics can play a big role in creating these safe, predictable, and sustainable food supply chains.

On this first part of a two-part episode of To the Edge and Beyond by Intel with Voice of B2B Daniel Litwin is Maria Meow, APAC Hospitality Vertical Marketing Manager for Intels Internet of Things Group, and Ankur Jain, Founder, and CEO of UdyogYantra Technologies, a company focused on bringing Industry 4.0 and its associated ecosystem of technologies to various industries, including the Restaurant industry.

When it comes to IoT, Meow has been at the intersection for over ten years at Intel. One thing shes noticed is that as technology has evolved, so has knowledge. In her role she is regularly relying on IoT to help improve food operations and to help ensure the trust and quality of food, which addresses a lot of consumer concerns.

With population is expected to increase globally by 10 billion in 2050, this creates a demand for creative food solutions, according to Jain. There is a need to have a safe, predictable, and stable food supply chain, Jain said. Internet of Things, artificial intelligence, and data analytics can play a big role in creating these safe, predictable, and sustainable food supply chains.

Intel technology is at the forefront of these IoT innovations, powering AI and analytics solutions like UdyogYantras to address these concerns for both consumers and restaurant brands.

The wheels of ghost kitchens were already in motion before COVID-19. But, their viability increased during the pandemic, as restaurants pivoted to meet consumer demand for BOPIS and third-party delivery. Now that things have settled a bit, restaurants are still sticking with some or all of the models developed during the pandemic.

Digital transformations have been accelerated ever since the pandemic began and have pivoted to delivery, pick up and drive through, Meow said. Digitally transformed brands will reap the benefits.

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Part 1: Quality Control in Delivery Only Ghost Kitchens Utilizing AI and Computer - MarketScale

The growing appreciation that human stockpickers struggle to outperform their benchmark indices has helped fuel a massive surge in assets held by passively managed exchange traded funds. Now some companies are hoping to show that artificial intelligence can finally give them an edge.

The technology is fast-evolving but at least two fund managers, EquBot and Qraft Technologies, running dedicated AI-powered ETFs are claiming early success, even though some of their AI models decisions might have required strong nerves to implement.

For example, the team at Qraft, which offers four AI-powered ETFs, listed on NYSE Arca, witnessed its technology build a weighting of 14.7 per cent in Tesla in its Qraft AI-Enhanced US Large Cap Momentum ETF (AMOM) in August last year, but when it rebalanced a month later on September 1 it sold it all.

The ETF began buying Tesla again in November, amassing a stake of 7.6 per cent by January this year, but in the February rebalancing it sold the entire holding once again. In each case when it sold it anticipated a sharp decline in the price of Tesla and was able to profit from the subsequent rise when it bought back in.

Alpha [excess returns above the market] is getting harder and harder to find, said Francis Oh, managing director and head of AI ETFs at Qraft, pointing out that humans can become emotionally attached to certain stocks, impeding their portfolio returns. Our model has no human bias.

Academic research has certainly shown that humans tend to be reluctant to crystallise losses, while conversely they feel driven to realise gains sometimes too early.

However, it is arguable whether the AI systems used by Qraft and EquBot can really be said to eliminate human bias because both are supported by large teams of data scientists who are constantly enhancing their models EquBot has teamed up with IBM Watson and Qraft has its own dedicated team in South Korea.

The machine only has historical data. It sees opportunities according to the rules it has been programmed for, Greg Davies, head of behavioural science at consultancy Oxford Risk, pointed out.

Chris Natividad, chief investment officer at EquBot, agreed: The system only knows what it knows, and its historical, he said, adding that in addition to humans deciding what new information the self-learning system should be given, data scientists also needed to check the outcomes so we can explain it to investors.

Both of EquBots ETFs, the AI Powered Equity ETF (AIEQ) and the AI Powered International Equity ETF (AIIQ) have outperformed their respective benchmarks since inception.

Similar successes have been notched up by Qrafts suite of AI powered vehicles. But the outperformance narrative is less clear when compared to the plain vanilla SPDR S&P 500 ETF (SPY) so far this year.

AMOM and Qrafts other funds, the Qraft AI-Enhanced Large Cap ETF (QRFT), the Qraft AI-Enhanced US High Dividend ETF (HDIV) and the Qraft AI-Enhanced US Next Value ETF delivered returns of between 15.3 per cent and 20.8 per cent during the eight months to the end of August. While respectable, none of them quite matched the 21.6 per cent return of SPY over the same period.

AIEQ also just undershot SPY, notching up a 21.3 per cent gain in the eight months to the end of August, while AIIQ delivered only 12.2 per cent.

Despite the unremarkable returns this year, Oh and Natividad remain convinced that their models have much to offer.

The velocity, variety and volume of data is exploding, said Natividad, adding that bringing in new data sources was a bit like adding more pixels to an online image. You get a clearer picture. He said asset managers and index providers were embroiled in an arms race for data.

Oh said value and momentum factors were becoming so short lived and fragmented that AI systems helped to find opportunities.

EquBot scours news, social media, industry and analyst reports and financial statements to build predictive models. It also looks at things such as job posts. Qraft also uses a variety of so-called structured and unstructured data sources to drive its models.

However, despite the promise of the technology, assets under management for the ETFs at both companies remain modest. AIEQ and AIIQ have less than $200m in assets between them, although Natividad said a partnership with HSBC which was using the two EquBot indices meant that there was $1.4bn tracking the strategies.

Qrafts ETFs have attracted less than $70m across all four vehicles, although Oh said the business model was once again focused on B2B advisory asset allocation modelling.

Rony Abboud, ETF analyst at TrackInsight, said investors probably wanted to know more. He emphasised the importance of due diligence, adding: The more data points used, the higher probability of having an error. So where do they get their data from and how accurate is it?

Despite misgivings, adoption of AI techniques in the investment world is increasing. Natural language processing is certainly a growing area, said Emerald Yau, head of equity index product management for the Apac region at FTSE Russell, which has made its first foray into NLP-powered offerings with its launch of a suite of innovation-themed indices.

However, Oxford Risks Davies warned that while algorithms were good at finding arbitrage opportunities they could not deal with ambiguity.

The problem with the investing world is the rules are not static, he said, adding that humans still retained an edge. If humans learn one thing in one context they can transfer it to another.

Visit our ETF Hub for investor news and education, market updates and analysis and easy-to-use tools to help you select the right ETFs.

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AI adoption in the ETF industry begins to grow - Financial Times

If youre a professional drone pilot who routinely uses flying robots in complex environments, you would want to check out this Early Access Program being offered by Parrot. The drone maker is handing out the new ANAFI Ai drone for a loan period of two months to test users who can help the company to evaluate the machines 4G standards.

The European drone manufacturer is looking for professionals from inspection, construction, infrastructure, energy utilities, public safety, surveying, agriculture, and defense sectors to:

Inspect buildings with strong 4G connectivity during built-up urban missions. Map long distance electric power lines in 48 MP at 1fps. Quickly generate 3D models of a building just by clicking on its land register in the new FreeFlight 7 App. Create precise flight plans in complex environments thanks to a unique obstacle avoidance system. Benefit from the embedded Secure Element to protect sensitive data. Develop flight missions with AirSDK and contribute to Parrot open-source piloting App.

In return, youd be expected to stay in contact with the Parrot team throughout the loan period, sharing datasets, photogrammetry 3D models, flight logs, and usage feedback.

Readers may recall, the ANAFI Ai is the first commercial drone to use 4G LTE as the primary data link between the drone and the operator. The bird features a uniquely designed omnidirectional obstacle avoidance system, 48 MP imaging sensor, 4K 60fps videos, and up to 32 minutes of flight time in an airframe that weighs less than 2 pounds.

In the United States, Parrot is teaming exclusively with mobile network provider Verizon and its drone software subsidiary Skyward to help operators utilize 4G out of the box. However, despite the splash made by this first-of-its-kind connected drone, some industry experts have questioned whether a 4G connectivity premium would actually be worth its price. The results from this program would, no doubt, help to answer those questions as well.

Interested? You can apply for the ANAFI Ai 4G drone Early Access Program by filling in this form here.

Read more: Sony Airpeak S1 drone hits pre-order in Japan; US launch soon

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Parrot wants pro pilots to test its new ANAFI Ai drone - DroneDJ

Robots powered by artificial intelligence could farm more sustainably than traditional agriculture, claims one Silicon Valley company.

Agricultural technology start-up Iron Ox says that its mission is to make the global agriculture sector carbon negative. And they have just secured 47 million ($53 million) from investors including Bill Gates.

CEO Brandon Alexander can't be accused of lacking experience when it comes to food production. He spent every summer of his childhood on his grandparents farm, picking cotton, potatoes, or peanuts under the Texas sun.

Yet, with a degree in robotics "precisely to escape farm work", Alexander says he couldn't shake the feeling that he could have a bigger impact working in agriculture.

A feeling that only grew when he learnt that 40 per cent of food grown worldwide is thrown away before reaching our shopping baskets.

Alexander left his previous job in 2015 to take a six-month road trip through California. He wanted to discover first-hand what problems American farmers were facing and figure out how automation could help.

Along the way, he learnt about extreme water scarcity, difficulties of finding labour, and a whole host of other issues. Armed with this knowledge, Alexander launched a startup focusing on autonomous farming in 2018.

"To really eliminate waste, to really get to that next level of sustainability and impact, we have to rethink the entire grow process," Alexander explains.

Now, in the company's greenhouse in Gilroy, California, two robots named Grover and Ada grow basil, strawberries and other crops using a hydroponic system.

Iron Ox's system uses artificial intelligence to ensure each plant gets the optimal levels of sunshine, water, and nutrients. Grover is in charge of carrying the plants to the dosing station, where it hands off the produce to Ada, a robotic arm.

The stop is akin to a doctor's visit. Sensors help to check the water for nutrients levels and any other ingredients needed for healthy growth. After the examination, customised doses of nutrients can be automatically delivered to the plants through the hydroponics system.

This way, the farm produces less waste and uses only the amount of water it really needs. Data is continuously collected from the crops helping the AI learn what the plants need - improving their yield and reducing their environmental impact.

Watch the video to see these AI-powered robots in action.

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Could these AI robots replace farmers and make agriculture more sustainable? - Euronews

Utilizing digital marketing when selling real estate is key to ensure that you optimize your sales operations. However, the digital landscape is changing rapidly and as AI solutions are becoming more and more powerful, the industry needs to be constantly alert to the commercial opportunities that come with them. Implementing AI and automation into your digital marketing strategy will provide competitive advantages and create more streamlined processes.

Recent statistics from Statista document that 97% of all home buyers in the United States in 2020 used the internet for home searching. This percentage alone is a reason to investigate if your business is reaching the full potential of its digital marketing strategy.

Because of low mortgage rates and remarkably high demand compared to supply, much due to the pandemic and work from home opportunities, the housing market in the US is booming right now. However, this is not the case for all areas and for all housing types. And like all cycles, this sentiment will change again, either slowly over time or due to an unforeseen disruption in the market.

Your real estate business will be much better equipped to perform well throughout any type of cycle or market condition with a proactive digital marketing strategy in place. By investing in an AI-driven and automated digital marketing solution, you will increase your chances of delivering superior results to your clients while streamlining the internal operations for your agents.

Marketers own scientific research from 54,000 property transactions carried out over a period of almost three years documents that the implementation of AI and automation in your marketing operations can result in an average return of 13.5 times the marketing investment for one of the most normal housing types. If the ambition is to do well for your clients, such results cannot be ignored by real estate Realtors.

For any successful property transaction, the goal should always be to identify, get in contact with, and present the given object to the one(s) that are most interested in the property. So, the million-dollar question is how to accomplish that, and how to do it in the most effective way. A defined strategy for targeting the most likely interested audience is key for converting perceived interest to real leads, and ultimately to achieve the best possible result for both the home seller and the realtor. A firm focus on quality rather than quantity in your approach to leads accumulation will increase the chance for utilising the potential of each individual property. To succeed with this, there is no alternative to advanced, automated solutions and efficient use of relevant data.

A proven digital marketing solution, with integrated automation and AI capabilities, will normally have all the tools and capabilities to run a streamlined marketing operation for both small and large real estate firms. The results will be immense for those that transition from more traditional and manual marketing operations and substantial for those moving from less advanced digital solutions. Accordingly, agents will also free up a lot of time, enabling them to focus more on what they normally are good at: connecting directly with potential buyers.

Marketing properties through an AI-powered, automated platform includes many key parts. The most important are (1) finding the right target audience, (2) tailor the communication thereafter, and (3) advertise in the right places at the right time. Your digital marketing solution will also conduct all necessary campaign management following the launch of a campaign, including optimisation and making sure that the overall budget is spent in the best possible way.

By combining historical data, AI-driven technology, and real-time analysis, Marketers intuitive products ensure you market your properties with the right content, to the right people, at the right time, and in the right channels. The benefits for both you and your clients can be immense. Curious to learn more? Click here to read more about the positive effect of using Marketers AI-powered platform for selling real estate

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Sell real estate better with AI and fully automated marketing - Inman