Category Archives: Ai

Today, artificial intelligence (AI) seems to be taking over the world and is slowly seeping into every major branch of industries worldwide. So what exactly is AI, and why is it necessary to hire AI engineers that offer the best-in-class technology? Lets dive in a little on why AI engineers are so in-demand.

As one of the fastest-growing technologies that is emerging across the world, AI technologies work with incredible finesse and speed by using machine learning. AI technologies can assist engineers in breaking down departmental bottlenecks and successfully managing data to extract insights. AI systems can automate low-value, repetitive jobs, allowing engineers to focus on higher-value, more complex tasks. The majority of the ongoing frenzy around Artificial Intelligence is centered on Computer Vision applications.

While hiring AI engineers, companies should pay close attention to the previous projects that were executed successfully by candidates, their skills with different AI/ML models, programming languages, and libraries. Along with this, companies hiring AI engineers must also ensure that the candidates possess efficient communication skills, the ability to meet deadlines, a constant thirst for new challenges, and excellent problem solving and analytical skills.

AI developers with an extensive portfolio, working on small personal projects all the time who can transmit their experience and demonstrate their hands-on engagement in solving an issue connected to the companys project, are the most highly valued. A good understanding of data analysis and data modeling may be essential for some projects. Combining all these skills enables developers who dabble with AI in engineering to carry out their responsibilities efficiently. Lets take a quick look at some of the tasks carried out by AI engineers.

AI engineers play a critical role in modern business, medicine, technology, science, and the economy, particularly in industries and domains where Artificial Intelligence technology has already had a substantial impact. While working with startups and niche industries present unique challenges for AI engineers, the fundamental tasks and responsibilities of AI jobs remain the same:

Using strategies linked with AI logic and ambiguity to set, achieve, and expand goals

AI technologies generate or can potentially generate high value, as AI/ML is one of the leading forces transforming our future

Improve operations, product or software development, and service delivery, among other things, by utilizing AI-based technologies

AI engineers exhibit cross-disciplinary perfections that include statistics, programming and industry knowledge, etc.

Use problem-solving skills to ensure that AI systems or infrastructures are correctly integrated within a company

Undertaking problem-solving initiatives that use logic, analyzing probabilities and statistics, and incorporating the latest Machine Learning techniques

Analyzing AI methods to improve the monitoring, testing, and management of development activities

Using AI best practices to build and develop scalable applications including voice recognition, chatbots, data processing, data mining, robotic control, and more

In addition to the above-mentioned tasks, an AI engineer also dabbles extensively with Natural Language Processing (NLP), analyzing Data, building infrastructure for Data Science, designing AI dependent software, and creating and deploying AI algorithms. As a result, AI developers are in extremely high demand among companies that aim to become the best in the industry.

Artificial intelligence is being used by businesses to boost staff productivity owing to its ability to automate repetitive operations across a company, freeing up staff to focus on innovative solutions, complex problem solving, and high-impact, more rewarding work. AI also presents businesses with new market opportunities and avenues to communicate better with customers, for e.g. chatbots. Chatbots have lately developed as a new channel for marketers and customers to communicate.

Without extensive and detailed data, its hard to run a successful firm. Even if companies manage to lay their hands on reliable data and metrics, finding someone to read all of that data and deliver valuable insights is even more difficult. However, one of the advantages of AI is that it can assist businesses in deciphering their data and gaining important and reliable insights.

AI applications that provide big data insights can assist businesses in identifying significant changes in patterns, isolating trends, and creating detailed reports. This helps enterprises to keep track of their operations, perform deep-content analysis & evidence-based reasoning, among other things. AI also helps businesses to identify any changes in the customers behavior that might affect the business productivity while also predicting what key business metrics the company will need to watch out for to optimize performance.

Artificial intelligence has made it feasible and extremely easy for businesses to gain insights from data, which previously required a lot of time and manual work. This enables companies to direct their employees energies elsewhere and make better company decisions.

While AI will undoubtedly neutralize some jobs, this had happened long before AI arrived on the scene. To give a few relevant examples would be door-to-door surveyors, bookkeeping and data entry, telephone operators, elevator operator, and manufacturing, box assemblers have all been discontinued or eliminated in the last century. Meanwhile, new job titles have evolved, such as AI-based agriculture services, app developer, digital-based elder care services, social media director, and data scientist, among others. Rather than replacing workers, AI can be used to assist them in doing their jobs more effectively.

By utilizing AI technology, companies can lay down their worries about data security and focus their attention on increasing productivity. AI engineers ensure that they consider data confidentiality and integrity as a top priority and are incredibly skilled in data and information security.

Utilizing AI systems and technologies also enables companies to tap into a more extensive source of information, giving them access to previously limited resources due to human capabilities, thereby allowing them to branch out into previously unheard directions.

With the practical applications of AI technology, the manual time required to execute time-consuming duties has been drastically reduced, from gathering information to commencing the process of filing consumer complaints. It has also improved the client experience in general. Reducing administrative activities will also aid in reducing human error manufacturing, operations, and execution of tasks.

When we apply this logic to the hundreds of repetitive tasks that are usually executed daily, the time-saving factor is significant. When a task is moved from being done manually to being done by software, the process time is significantly reduced. Mechanical and often dull tasks that would generally take employees days to complete may now be finished in minutes or hours, allowing employees and developers to focus on more meaningful and creative work.

As a result, employees will be more productive and satisfied at work, and the company will be more profitable.

With Turing, companies can remotely hire AI engineers, pre-vetted for a Silicon Valley bar in a matter of days. Turing has created the first ever Intelligent Talent Cloud that uses AI to source, vet, match, and manage 1 Million+ developers across the globe. Turings AI-powered system increases the quality of hire by intensively analyzing the potential candidates experience, expertise in tech stacks, seniority level, and eliminates human bias, resulting in hiring the best-suited candidate for the job. This enables companies hiring AI engineers to access a talent pool of the top 1% of 1 Million+ developers with strong technical and communication skills, ready to work in their time zone. Not just this, Turing also offers a two-week, risk-free trial period to make sure the developers deliver to companys standards.

In 2020, the AI field and its technical expertise will continue to dominate the job market across all industries, and there seem to be no signs of it slowing down. While some companies prefer to hire freelance AI developers, it is always beneficial to hire full-time employees who can take ownership of AI projects and be accountable for their tasks. The top companies across notable sectors are always on the hunt for talented AI engineers who have demonstrable experience in their pockets. That alone is a compelling argument for companies to hire AI engineers immediately and join ranks with the big-shots of the industry.

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How to hire AI engineers and why are they in such high demand? - AZ Big Media

Smart transportation and intelligent communication network of things, wireless connection ... [+] technologies

In the first blog in the AI transportation and logistics series, I featured AI transformation innovations at Purolator; the second blog focused on the acceleration of smarter AI telematics in fleet management. The third blog explored AI emotion sensors and the impact that the affective computing market is having on the transportation and logistics industry. This fourth blog discusses AI revenue growth and operational optimization use cases to ensure your T&L company is positioned for accelerated growth.

AI : Revenue Growth and Operations Optimization Use Cases

Why is this important?

First, the digital transformation of a supply chain is one of the most important investments a T&L company can make to ensure it understands upstream to downstream, and the interdependencies to be able to optimize decisions in real-time. T&L supply chains have tremendous overhead in moving to cloud enablements to easily access to all data sets from diverse vendors to create a more connected enterprise.

Companies like FedEx have recently joined forces with Microsoft to improve its analytics to evolve its offerings to commercial customers.The shipping giant is combining its in-house IoT technology with Microsoft 365 and Azure cloud services. This will further allow Fedex to integrate machine learning and artificial intelligence in its systems to strive to achieve more real-time logistics and inventory management, and provide a more efficient customer supply chain.

In addition to near real-time item tracking, FedEx customers will receive intelligence on global shipping conditions and potential disruptions, such as natural disasters. This new service is designed for customers whose shipping requirements are time-sensitive: like medical supplies, products for research, plants or other perishable goods.Information on geographic availability real time customer visibility is also advancing. FedEx is also pioneering in robotics and AI to further shake up the logistics landscape - with estimates of the global market value varying between $8 trillion and $12 trillion -as they squarely monitor their growth vs market leaders like: Stamps.com, Owens & Minor, DACHSER, BB, CEVA Logistics, Royal Mail, DHL, C.H. Robinson, Deutsche Post DHL Group, UPS and Purolator.

FedEx also recently took part in a drone delivery pilot alongside Google's sister company Wing Aviation LLC. Last year, FedEx announced a prototype of the SameDay bot, a battery-powered autonomous vehicle that relies on artificial intelligence to navigate safely through the streets of Memphis (FedEx headquarters location).

UPS also recently announced this year, their pioneering drone deliveries, optimized routes and expansion of their GreenFleet of more than 12,000 vehicles.

Other North America leading brands, like Purolator have announced their commitment to innovation, by announcing plans to invest in more than $1 billion with a five-year futuregrowth and innovation strategy. As stated In recent years, dynamic market shifts driven by e-commerce and technology have changed the way businesses and consumers buy, sell and exchange goods. E-commerce sales are expected to reach $4.88 trillion worldwide by 2021. In the age of convenience, consumers want their packages faster with more visibility, control and flexibility throughout the supply chainTrading relationships are shifting around the globe, creating new opportunities for businesses of all sizes to expand to, from and within Canada. What I like about Purolators announcement is the clarity of their commitment to innovation and sharing their action plan. See More Here.

The mix of skills and talents to evolve a T&L digital SCM transformation program is one of the most challenging and exciting opportunities for disruption.

However, integrated a robust enterprise-wide AI strategy across all a T&L industrys data assets across its value chain will require tremendous investments, and focus to ensure that an enterprise analytics architecture is designed and is easily made accessible to decision makers.

AI is all about asking the right questions that you want to solve, and ensuring that you have unified access to the right data, effectively labelled, to advance optimizing insights and decision making.

Below are AI SCM use cases relevant to the transportation and logistic industry.

As a board director or a CEO, ask yourself are you able to perform these network operations, identify your bottlenecks, and identify opportunities for optimization across the supply chain value-chain?

1.)Supply Chain Resiliency

a.Can you simulate or traverse your operating costs (or bill of materials) to proactively surface supply chain disruptions?

b.Can you easily evaluate in what if scenarios and assess the downstream impact, and simulate to support risk mitigation?

2.) Supply Allocation

a.Can you easily source and allocate limited resources and apply them to the most valuable resourcing areas to drive the maximum allocations?

b.Can you easily shift your mix of products (whether that be a highest margin product, most important customer, or most fragile part of the supply chain) and see the ripple implications?

3.) Working Capital

a.Can you easily identify the total value of working capital across business units?

b.Can you identify surplus inventory, and easily access scenario-based planning, and secure smart modelling (market simulation dynamics) to improve decision making to maximize your working capital.?

4.) Demand Planning

a.Can you easily integrate your sales forecasting data with additional sources such as actual demand, inventory levels, and marketing /pricing promotions?

b.Can you easily analyze your demand planning forecast, have smart alerts on deviations, and allow your planners to proactively adjust or override in real time other forecasts?

5.) Portfolio Optimization and Pricing?

a.Can you calculate end-to-end SKU-level profitability and reconcile portfolio dynamics?

b.Can you enable profitability portfolio management, scenario planning, and have real-time proactive alerts to alter operational decision making to maximize profits?

6.) Carbon Planning

a.Can you easily quantify and reduce environmental impact while building a resilient supply chain operation?

b.Can you set and monitor collaborative carbon plans across all of your operational divisions?

There are many more T&L supply chain questions relevant to advance an AI Enterprise Journey the race is on and it is an exciting one. Love to hear from you on what other AI questions that you are working on.

Conclusion

If you are a CEO or a Board Director of a transportation and logistics company, where do you stand in these key self-reflection innovation and leadership questions. In summary, the transportation and logistics industry is undergoing tremendous change.

Are you ready for this digital end-to-end connected enterprise - supply chain management - reality in your logistics and supply company?

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Advancing AI Revenue Growth And Operations Insights In The Transportation And Logistics Industry - Industry Series: Blog #4 - Forbes

This article is part of our series that explores the business of artificial intelligence.

Like every year, Adobes Max 2021 event featured product reveals and other innovations happening at the worlds leading computer graphics software company.

Among the most interesting features of the event is Adobes continued integration of artificial intelligence into its products, a venue that the company has been exploring in the past few years.

Like many other companies, Adobe is leveraging deep learning to improve its applications and solidify its position in the video and image editing market. In turn, the use of AI is shaping Adobes product strategy.

Sensei, Adobes AI platform, is now integrated into all the products of its Creative Cloud suite. Among the features revealed in this years conference is an auto-masking tool in Photoshop, which enables you to select an object simply by hovering your mouse over it. A similar feature automatically creates mask layers for all the objects it detects in a scene.

The auto-mask feature saves a lot of time, especially in images where objects have complex contours and colors and would be very difficult to select with classic tools.

Adobe has also improved Neural Filters, a feature it added to Photoshop last year. Neural Filters use machine learning to add enhancements to images. Many of the filters are applicable to portraits and images of people. For example, you can apply skin smoothing, transfer makeup from a source image to a target image, or change the expression of a subject in a photo.

Other Neural Filters make more general changes, such as colorizing black-and-white images or changing the background landscape.

The Max conference also unveiled some preview and upcoming technologies. For example, a new feature for Adobes photo collection product called in-between takes two or more photos that were captured at a short interval of each other, and it creates a video by automatically generating the frames that were in-between the photos.

Another feature being developed is on point, which helps you search Adobes huge library of stock images by providing a reference pose. For example, if you provide it with a photo of a person sitting and reaching out their hand, the machine learning models will detect the pose of the person and find other photos where people are in similar positions.

AI features have been added to Lightroom, Premiere, and other Adobe products as well.

When you look at Adobes AI features individually, none of them are groundbreaking. While Adobe did not provide any architectural or implementation details in the event, anyone who has been following AI research can immediately relate each of the features presented at Max to one or more papers and presentations made at machine learning and computer vision conferences in the past few years. Auto-masking uses object detection and segmentation with deep learning, an area of research that has seen tremendous progress recently.

Style transfer with neural networks is a technique that is at least four years old. And generative adversarial networks (GAN), which power several of the image generation features, have been around for more than seven years. In fact, a lot of the technologies Adobe is using are open source and freely available.

The real genius behind Adobes AI is not the superior technology, but the companys strategy for delivering the products to its customers.

A successful product needs to have a differentiating value that convinces users to start using it or switch from their old solutions to the new application.

The benefits of applying deep learning to different image processing applications are very clear. They result in improved productivity and lower costs. The assistance provided by deep learning models can help lower the barrier of artistic creativity for people who dont have the skills and experience of expert graphical designers. In the case of auto-masking and neural filters, the tools make it possible even for experienced users to solve their problems faster and better. Some of the new features, such as the in-between feature, are addressing problems that had not been solved by other applications.

But beyond superior features, a successful product needs to be delivered to its target audience in a way that is frictionless and cost-effective. For example, say you develop a state-of-the-art deep learningpowered neural filter application and want to sell it on the market. Your target users are graphic designers who are already using a photo-editing tool such as Photoshop. If they want to apply your neural filter, theyll have to constantly port their images between Photoshop and your application, which causes too much friction and degrades the user experience.

Youll also have to deal with the costs of deep learning. Many user devices dont have the memory and processing capacity to run neural networks and require cloud-based processing. Therefore, youll have to set up servers and web APIs to serve the deep learning models, and you also have to make sure your service will remain online and available as the usage scales. You only recoup such costs when you reach a large number of paying users.

Youll also have to figure out how to monetize your product in a way that covers your costs while also keeping users interested in using it. Will your product be an ads-based free product, a freemium model, a one-time payment, or a subscription service? Most clients prefer to avoid working with several software vendors that have different payment models.

And youll need an outreach strategy to make your product visible to its intended market. Will you run ads on social media, make direct sales and reach out to design companies, or use content marketing? Many products fail not because they dont solve a core problem but because they cant reach out to the right market and deliver their product in a cost-efficient manner.

And finally, youll need a roadmap to continuously iterate and improve your product. For example, if youre using machine learning to enhance images, youll need a workflow to constantly gather new data, find out where your models are failing, and finetune them to improve their performance.

Adobe already has a very large share of the graphics software market. Millions of people use Adobes applications every day, so the company has no problem in reaching out to its intended market. Whenever it has a new deep learning tool, it can immediately use the vast reach of Photoshop, Premiere, and the other applications in its Creative Cloud suite to make it visible and available to users. Users dont need to pay for or install any new applications; they just need to download the new plugins into their applications.

The companys gradual transition to the cloud in the past few years has also paved the way for a seamless integration of deep learning into its applications. Most of Adobes AI features run in the cloud. To its users, the experience of the cloud-based features is no different than using filters and tools that are directly running on their own devices. Meanwhile, the scale of Adobes cloud makes it possible for the company to run deep learning inference in a very cost-effective way, which is why most new AI features are made available for free to users who already have a Creative Cloud subscription.

Finally, the cloud-based deep learning model provides Adobe with the opportunity to run a very efficient AI factory. As Adobes cloud serves deep learning models to its users, it will also gather data to improve the performance of its AI features in the future. For example, the company acknowledged at the Max conference that the auto-masking feature does not work for all objects yet but will improve over time. The continued iteration will in turn enable Adobe to enhance its AI capabilities and strengthen its position in the market. The AI in turn will shape the products Adobe will roll out in the future.

Running applied machine learning projects is very difficult, which is most companies fail in bringing them to fruition. Adobe is an interesting case study of how bringing together the right elements can turn advances in AI into profitable business applications.

This article was originally published by Ben Dickson onTechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech, and what we need to look out for. You can read the original articlehere.

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How AI is driving powerful new Photoshop features and shaping Adobes product strategy - The Next Web

Global business logistics transport image

In the first blog in the AI transportation and logistics series, I featured AI transformation innovations at Purolator; the second blog focused on the acceleration of smarter AI telematics in fleet management. The third blog explored AI emotion sensors and the impact that the affective computing market is having on the transportation and logistics industry. The fourth blog discussed AI revenue growth and operational optimization use cases relevant to the T&L industry and touched on drones and their economic impact.

This fifth blog discusses the impact of smarter intelligence integrated into the supply chain management (SCM) value chains, from picking, sorting, delivering, and all sales and customer service operations. A complete technology end-to-end connected - IoT a smarter AI sensor value chain, where humans are the strategic architects, controlling the dynamics of their SCM ecosystems, determining and adjusting in real-time operational requirements.

We will see in our life time, more robots, cobots, driverless vehicles and smarter IoT sensor highways integrated creating this realtime pulse that will reshape millions of jobs around the world.

Sound far-fetched. Likely in my life time, I wont see all of this come together. However, my children likely will.

Just look how at some history of the origin of the aerospace industry which dates to1903when the Wright brothers demonstrated an airplane capable of powered, sustained flight. The worlds first scheduled passenger airline service took off in 1914, operating between St. Petersburg and Tampa, Florida. This historical event helped advance daily transcontinental flights.

In just 100 years, we went from literally no airplanes in the sky, to having over 250 international airlines, and over 5,000 airlines having official ICAO codes.

Lets compare this speed to the speed of the drone and IoT smart logistics industry transformation. What are the drivers of the change. Perhaps four key drivers are noteworthy 1) the disappearance of space and time barriers 2.) the ability to stay connected while on the move 3.) The Internet of Things and 4.) The universality of the internet and rise of cloud computing enablements. The outcome - smarter connections in everything.

We are underway with a transformation where our customers expect faster deliveries, real-time information available on any of their devices, smartphones, tablets or elsewhere and ability to alter suppliers on any whim they so choose. The power has shifted to the customer driving the logistics industry to rethink almost everything.

The digitalization of the transport and logistics industry enables supply chain companies to have not just better supply chain visibility, real-time management of traffic and cargo flows, simplification and the reduction of administrative burden, better efficiencies of costly infrastructures and resources, but more importantly this full scale digital transformation will open up new value growth opportunities and reduce the overall carbon footprint of transport end to end.

Pioneers like former Uber employees tackled the freight forwarder sectorby founding the company Beacon, affirming its claim to a place in the logistics organization but also in retail financing. Beacon has recognized that shippers are seeking technology-led products and services that will meet their needs more effectively, enhance their experience and cut their costs. Ovrsea, a digital air and maritime freight in France, is innovating bringing rich analytic insights across ocean freight, air freight, rail freight - across complex logistics and transportation networks enabling unprecedented visibility.

Frost & Sullivanestimates that the market that they define as Truck-as-a-service (TaaS) is expected to reach $79.4 billion in 2025 in the United States, against $11.2 billion currently.

In a white paper titledFuture of the Tech Economy, UBS estimated that warehouse rent is no longer the main driving force behind decision-making. Prologis, one of the largest owner of logistical warehouses in the world (1.7% of the world's GDP passes through Prologis warehouses each year), indicates online sales will require three times more warehouse space than the traditional economy.

Just look what has happened in the changing shift from B2B deliveries to B2C mix during Covid-19, disrupting supply chains around the world, creating unprecedented shipping backlogs, accelerating costs, and consumer disappointments.

With this type of change, the affordability economics in warehouses controlled by people no longer will be feasible. Hence the rise of drones and accelerated usage of alternative approaches to servicing transportation and logistical needs.

McKinsey indicates that 80% of all commercial vehicles will be networked by 2030, providing great potential for the emergence of additional digital services. Boston Consulting Group suggests that the provision of networked digital services will increase tenfold. You cannot pick up one of the major SCM publishing sources without some mention of AI, drones, robots or rise even of sentient beings.

The digitalization of the management of transport and logistic companies like: giants - FedEx, UPS, etc. - are having to respond to improving the productivity, safety, and profitability of their operations. Being able to harness datawith advanced analytical and connected tools can track all the activities of the carrier: from the management of driving and stopping times, to the braking and safety driving risks to the forecasting of the maintenance of trucks and their tires, and even through to the administrative management, the organization of rounds, the geo-location of trucks, the real-time tracking of packaging the real-time tracking insights are endless.

The biggest challenge is assembling all the disparate data sources into a universal data or information knowledge hub. Without integration into all the digital and data rich ecosystems, mastering the sharing of data is a daunting task for this industry. Hence why data lineage and data labelling services are also in high demand.

Taking control of the data means capturing, recording, and organizing data provided by customers, or from systems, customers, drivers, trucks, etc. and knowing how to share the data insights to provide better service is a key to unlocking the change journey roadmap.

Based on my experiences to date, complex digital transformation in the T&L industry is all about vision, strategy and architecture and applying new technologies and imagining new optimizations and new services. Its easy to stay locked into the past without appreciating how fast the T&L industry is transforming. Its a very exciting team to get moving, pardon the pun - its Halloween night.

XPO Logistics, for example, has set up a collaborative cloud portal to exchange information between its shipper customers and transport providers. This portal makes it possible to optimize the flow and cost of freight transport and to forecast future transport needs by combining machine learning and predictive analytic tools.

In the United States, JB Hunt has unveiled a Shipper 360 portal that gives shippers access to multiple modes of ground transportation, as well as information on carrier performance.

We are also seeing the acceleration of dark warehouses since Covid-19; a dark warehouse is a fully automated warehouse that is equipped to handle inventory by following systems commands. Goods can be moved, sorted, or even packed by robots or other automated machines doing away with the need for intensive physical labour. Goods can be moved to almost any location within the warehouse using automated machines and robots.

Nowhere is there more digital innovation change using AI in the transportation and logistics industry than in China. Just look at JD-X, the logistics lab of Alibabas e-Commerce archival. JD has been developing diverse smarter applications related to the movement and processing of packages from autonomous drones, delivery robots, and unmanned or dark warehouses, which are facilities where robots work alone in the dark, completing tasks formerly done by humans.

Automating the logistics sector is a major global trend, but in China, the stakes are even higher as the countrys population rapidly ages and labor costs rise. According to Chinas National Bureau of Statistics, the number of workers between 16 and 59 years old plunged by over 5 million in 2015. As the labor pool shrinks, demands for better benefits and higher wages have alsorisen. China has recently increased its policy of one child to two children to start accelerating labour needs to fuel their economy.

Conclusion

If you are a CEO or a Board Director of a transportation and logistics company, where do you stand in thinking about completed intelligent supply chains, using AI, drones, driverless enablements and dark warehouses.

Is your company ready for a fully digital end-to-end connected smarter reality facing the transportation and logistics industry?

There is no better way to start, than to start.

Tracking your competitors is one powerful way to stay ahead of the game -but dont wait, perhaps a visit to China, or visit an Amazon turbo charged robot warehouse, or bring a number of freshly minted engineering students to your company operations and ask them what would they focus on to drive a breakthrough experience even better take out ten of your smartest high potentials and pull them out of their demanding full-time jobs to think hard about imagineering your future business model. Give them just 30 days to rethink what others simply have not had the courage to say.

You will learn and grow and more importantly these high potential employees will feel an abundance of new energy to advance your company forward. The race to modernize is on and its moving forward with you or without you.

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Advancing AI Smarter Intelligence Everything In The Transportation And Logistics Industry - Industry Series: Blog #5 - Forbes

Penguin Computing, aleader in high-performance computing (HPC) and artificial intelligence (AI), will offer customized designs for Intel Select Solutions for HPC and AI Converged Clusters. Penguin Computing, a long-time partner for Intel-based HPC systems, joined the Intel Select Solutions ecosystem in early 2020.

Increasingly, industries are utilizing the powerful combination of HPC and AI to solve their difficult data-driven problems, Matt Jacobs, Chief Strategy Officer of Penguin Computing explained. They need these new HPC and AI converged architectures to use machine learning to narrow the scope of the questions they need to ask of their modeling and simulation runs. With these new converged solutions, organizations are able to run both types of workloads in a single platform with technologies optimized for both types of computing.

Penguin Computing has been building customized, complex, built-to-order systems for nearly two decades. They deliver some of the fastest HPC systems in the world to leading scientific and commercial enterprises. Intel Select Solutions for HPC and AI Converged Clusters gives them a baseline architecture to continue to build their customized systems.

Intel Select Solutions are workload-optimized reference designs that minimize the challenges system architects face when building solutions for their infrastructure. This reference architecture comprises tailored combinations of Intel compute, memory, storage, and network technologies that are optimized to increase the value of hardware and applications investments. The solutions are validated and certified by industry builders, such as Penguin Computing, and verified by Intel. The Intel Select Solution for HPC and AI Converged Clusters addresses an emerging use case.

This demand for convergence is largely driven by new customers coming into the market who need these tools to design innovative products, Jacobs said. But they dont necessarily have the same understanding of these technologies that long-time practitioners do. Its a new generation of scientists and researchers finding unique ways to solve their problems with converged HPC and AI. So, they need these solutions to be easily adopted to accelerate their time to solution and productivity.

But technology evolution continuously introduces new approaches and tools to solve tomorrows scientific problems. Understanding new CPU capabilities, model frameworks, and software-defined architectures, among others, introduces new challenges and growing complexity to building optimal computational platforms. A baseline architecture helps make new solutions more easily adopted.

Its all about reducing complexity, Jacobs added. The scientific domains we deal with have quite different requirements. The hardware and software-defined technologies to address their needs continue to get more complex, and the approaches to build balanced compute, network, and data storage systems to solve their higher science and data problems can be quite varied. Starting with a proven reference architecture helps reduce complexity and increase availability of these platforms for customers who dont necessarily have a lot of technical expertise. A baseline architecture accelerates time to market and the customers time to solution.

According to Jacobs, having a reference architecture also eases the challenges many industries, including HPC solution builders, face with supply chains these days. As the complexity of systems increases, acquiring the best parts to evaluate becomes more difficult. Using the Intel Select Solution for HPC and AI Converged Clusters helps Penguin Computing ease those supply chain challenges with a validated, verified architecture of a reduced number of components.

Weve been working with Intel programs like this for a while, Jacobs concluded. With Intel Select Solutions, the ability to build more systems off fewer part numbers gives us greater supply chain efficiency, which allows us to deliver more solutions faster to customers. Additionally, these engagements provide customers access to the Intel teams to help on the engineering side and provides context and homogeneity in a highly configurable technology environment.

Learn more about Intel Select Solutions

Learn more about Penguin HPC and AI Clusters

Intel technologies may require enabled hardware, software or service activation.

No product or component can be absolutely secure.

Intel Corporation. Intel, the Intel logo, and other Intel marks are trademarks of Intel Corporation or its subsidiaries. Other names and brands may be claimed as the property of others.

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Penguin Computing to Offer Custom Designs for Intel Select Solutions for HPC and AI Converged Clusters - HPCwire

Amazons Pietro Perona and Prem Natarajan join UCLAs Leonard Kleinrock, Jayathi Murthy, Andrea Ghez, Jens Palsberg and Stefano Soatto in flashing thumbs-up signs during an Amazon Science Day at UCLA kickoff event. (UCLA Photo)

Amazon and UCLA are launching a research hub that will draw upon industry and academic research to address the social issues raised by the rapid rise of artificial intelligence.

The Science Hub for Humanity and Artificial Intelligence will be based at the UCLA Samueli School of Engineering in Los Angeles, with Amazon providing $1 million in funding for the initial year of the partnership. The two parties may renew the agreement for up to four additional years.

In a news release, UCLA said faculty from across its campus will collaborate with Amazons AI specialists to identify and solve research challenges in the field of artificial intelligence, with particular attention to issues such as algorithmic bias, fairness, accountability and responsible AI. The collaboration will support doctoral fellowships and research projects as well as community outreach programs.

We are delighted to collaborate with Amazon on this effort to examine the future of artificial intelligence and its implications for our world, UCLA Chancellor Gene Block said. The Science Hub for Humanity and Artificial Intelligence will advance AI-related discoveries and deepen our understanding of a discipline that is revolutionizing the way we use and understand modern technology.

The hub will support AI research under the guidance of an advisory group headed by UCLA computer science professor Jens Palsberg. The group, which includes representatives from Amazon and UCLA, will develop, solicit and select research proposals and review nominations for fellowship recipients.

Funding for the hub will support annual fellowships of $70,000 each for students in the second, third or fourth year of a UCLA Engineering doctoral program. Fellows will also be invited to take part in paid summer internships at Amazon.

The hub is designed to foster the educational mission of the university, so it can best educate the diverse talent needed to sustain the AI revolution in the years to come, in a way that benefits all sectors of society, said Stefano Soatto, vice president of applied sciences for Amazon Web Services AI. Soatto, who is currently on leave from his position as a UCLA computer science professor, was instrumental in helping Amazon and the university establish the science hub.

UCLA organized an Amazon Science Day event today to celebrate the unveiling of the hub as well as the 52nd anniversary of the birth of the internet. On Oct. 29, 1969, UCLA computer scientist Leonard Kleinrock directed the transmission of the first internet message from his lab to Stanford Research Institute. (The network crashed after Kleinrocks team sent the first two characters: the LO in LOGIN.)

The science hub is the latest example of Amazons collaboration with universities across the country to advance research in AI and other fields. Just this week, for example, Amazon Web Services spotlighted the debut of its quantum computing research center on Caltechs campus.

In Seattle, Amazons hometown, the University of Washington has received a healthy share of support: In 2012, Amazon established two $1 million endowed professorships in machine learning at the Paul G. Allen School of Computer Science and Engineering. And in 2016, Amazon provided $10 million in funding for a new computer science building.at UW.

At least six UW faculty members have been designated Amazon Scholars, which means they spend between 20% and 90% of their time at Amazon. One of those scholars is economics professor Pat Bajari, who is Amazons chief economist.

UW computer scientist Ed Lazowska said the university benefits from having Amazons headquarters in the same neck of the woods. Because Amazon is a 20-minute bicycle ride from campus, we have had no need to formalize a broad agreement with them, in contrast to universities located thousands of miles away, Lazowska told GeekWire in an email.

This report has been updated with further information about Amazons ties with UW.

Original post:

Amazon partners with UCLA on science hub focusing on AI and its social impact - GeekWire

More enterprise use of AI does not correlate to success of the business.

On Oct. 21, Deloitte released the fourth edition of its "State of AI in the enterprise" survey. The survey examined the practices of 2,875 executives from 11 countries. This year Deloitte looked at how successful enterprises were in deploying AI .

Deloitte found that while more and more enterprises are deploying AI, not all are successful.

In this Q&A about deploying AI in the enterprise, Beena Ammanath, executive director at Deloitte, says the successof AI in the enterprise relies on a number of factors, chief among them the team that's leading the enterprise's application of AI.

Why was one group of executives able to see such good outcomes despite their low deployment of AI in the enterprise?

Beena Ammanath: [They] clearly are the ones everybody wants to learn from. A few things that we started seeing was having an enterprise-wide AI strategy. They made AI a key element of business differentiation and success. The strategy came from the business leaders and not from the data scientists or the IT teams. Having the business leaders drive the strategy was one of the best practices.

The other one was really the focus on culture and change management. [Some executives] had invested heavily into change management because working with AI leads to new ways of working. That needs a cultural change, that needs some level of change management, and you need to be able to get all your employees on board, to be able to succeed with this new technology. So, culture and change management and putting a focus on that upfront was another we saw in this group.

The third one was really leaning into ecosystems and partnerships. The companies that leaned more heavily into the AI ecosystem, whether it's startups or academia or partners or vendors they're ahead.

Why does it matter for enterprises to have business leaders take charge of strategies for AI in the enterprise?

Ammanath: Even a few years ago when AI was just getting started and new, most of the ideas of how AI should be used within a business came from the data scientists. They understood the technology and they felt that they could guide on what should be done with the technology. It was very tempting for the business leaders to let the data scientists lead the application of the technology. Now we see more and more pivot into putting the business requirements first. Even a few years ago, the question would be, 'Oh I have all this data, what insights can I get from it?' The question has to change into: 'You know I have this business problem, how can I solve it with AI, and data?' So, starting with the business problem, starting with the business requirements, and then figuring out which technology to use, that's the nuance behind it.

Can this lead to problems?

Ammanath: At the end of the day, there may not be a technology solution right for the business problems. It becomes a partnership between the technology team and the business leader but being led by the business leader. And I've personally been in scenarios where there is a business problem but it's not something that can be solved with data and AI. The technology is just not mature enough. The most successful companies are the ones that have that close partnership between the technology team and the business team.

Most companies still see AI as a competitive edge, and if they don't invest in AI they'll be left behind. Beena AmmanathExecutive director, Deloitte

For enterprises that are underachievers, and their large-scale deployment of AI did not result in positive outcomes, does this discourage their use of AI in general?

Ammanath: I guess it's a bit too early still. Most companies still see AI as a competitive edge, and if they don't invest in AI, they'll be left behind. So, across the board they are planning to continue to invest in AI, and some of the best practices that we're putting out hopefully help them move up from underachiever status. They are continuing to invest, and they do see AI as a competitive edge so it's not, they're not disillusioned yet.

For enterprises that are just starting, what is the reason for their late deployment of AI?

Ammanath: They just did not start early enough. They're also the group that's least likely to demonstrate some of the best practice behaviors. They will average about 1.6 out of 10 possible full-scale deployments of different types of AI. They are purely lagging. And what we've seen is the late start has impacted their ability and they must do a bit of catchup.

I think we're going to see, based on the best practices that's coming out and where the field is, they might be able to accelerate. Even though they got a late start, they're more likely to accelerate because the industry has matured. The best practices are coming out more frequently so they hopefully don't have to experiment as much, they just have to get their foundation base set up, and then they can move faster.

Editor's note: This interview has been edited for clarity and conciseness.

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Strategies to successfully deploy AI in the enterprise - TechTarget

SCIENCE FICTION has historically had a contentious relationship with scientific development at the same time that technological products coming onto the market have long relied on rosy narratives for their further popularization. AI 2041: Ten Visions for Our Future (Currency, 2021), the latest collaboration between science fiction author Chen Qiufan and former head of Google China and current CEO of Sinovation Ventures, Kai-Fu Lee, sits solidly at the intersection of these narrative selling points and a selling point is exactly what is being made here. Artificial intelligence, Lee claims in the introduction, is the worlds hottest technology, while Chen posits that [f]rom the past era to the present day, the unstoppable force of AI has been revolutionizing every dimension of human civilization. Enthusiastic about their vision for a world transformed by AI, the two collaborated on what would become a sort of call-and-response example of scientific fiction (as opposed to science fiction) in which Chen contributed 10 short stories focusing on individual aspects of AI technology set within the next 20 years, while Lee follows up with an explanation of that technology in the present how it works, how it is applied, and how it is likely to develop in the coming decades. As a textual product, it is both entertaining and informative, imagining a world that feels at once both familiar and radically different from our own. For all its entertainment value, however, the ethics of a project that attempts to imagine a future in which AI is a central driver of progress are insufficiently examined, resulting in a text that is techno-utopian at best and deliberately obfuscatory at worst. In fact, given Lees enormous investment in ensuring that the public accepts AI technologies developed by his various companies, the stakes of this project seem to be less about speculative futures and more about neutralizing potential objections on the part of consumers in the present.

At the heart of AI 2041 is fictions presumed ability to imagine, predict, and, at some level, shape the future. Outlets like Wired report that Chinese science fiction authors like Chen Qiufan have been elevated to the status of oracles, with his work described as science fiction realism, a term used since the 1950s to characterize science fiction that critically evaluates the present level of technology and society. The idea that SF does something is increasingly being taken up in science and technology studies, too, with a special issue of Osiris dedicated to science fiction and the history of science, including two articles devoted to Chinese science fictional forms and their real-world effects. International policy historian Julian Gewirtz has written a well-regarded article on how Alvin and Heidi Tofflers Future Shock influenced Chinese science and technology policies in the period of reform and opening-up that began in the 1980s; today Chen is widely seen as the successor to this type of science fictional speculation in the tech world. Chens quasi-prophet status is central to AI 2041s conceit that it might not be long until these stories become all too true.

As such, the selling point of the book is that, while its science fictional visions are themselves only imaginary, they draw on real technology and represent real possible paths for the development of AI. And not in a distant future, either: AI 2041 takes its name seriously. Lee claims that the technologies in the book have at least an 80 percent likelihood of coming to pass by 2041, with the book representing a responsible and likely set of scenarios. This prediction may seem far-fetched, but readers are likely to recognize many of the technologies already present in their own lives here in 2021: in the first story, The Golden Elephant, for example, Chen introduces an insurance company that uses deep learning technology to make predictions affecting individual premiums, while in Contactless Love, players find love through participating in online roleplaying games amid the ravages of COVID-19.

AI 2041, then, with its unique marriage of factual explanation and fictional short stories, joins the rank of a genre that will be familiar to most readers: the techno-utopian promise. But it is perhaps more legible through the lens of technological solutionism, a term coined by tech writer Evgeny Morozov to describe the idea that complex social phenomena everything from politics to education to health care to agriculture can be understood as neatly defined problems with definite, computable solutions or as transparent and self-evident processes subject to easy optimization: [I]f only the right algorithms are in place. The ideological framework of solutionism shifts our view of the world to redefine things such as inefficiency or human relations as problems with a technological solution. The technology itself, Lee and Chen claim, is neutral; it is human behavior that is unethical or inefficient, requiring technological intervention. We see this shift toward neutral technological solutionism in Twin Sparrows, for example, a story in which a characters autistic behavior is mollified not through a change in social or kinship structures, but through the introduction of an AI companion that can uniquely understand him. Lees exuberant promotion of AI technologies as the solution to most existing social problems not only addresses those problems themselves, but also redefines human behavior as a problem in and of itself that can be solved through the application of neutral or even benevolent AI.

In fact, AI is explicitly and frequently described by Lee throughout the text as neutral an objective technology that only acquires ethical value through its use by humans. This is a popular idea in technological innovation spheres, and it is also a wrong one. Because such technologies are developed by private corporations and their IP holdings are heavily guarded, it can be almost impossible to discover how a particular artificial intelligence or algorithm was designed, what dataset or corpus helped build it, or even how it works at all. The act of developing an AI involves training it on massive quantities of data, and as Lee points out in his explanation of deep learning, the program comes to recognize patterns that guide its predictive abilities. When the predicted outcomes or patterns replicate human biases inherent in the original data, the algorithm recreates systemic, repeatable errors resulting in the perpetuation of discrimination. Lee and Chen recognize this the very first story in the collection, the aforementioned The Golden Elephant, deals with it explicitly, in fact but they attribute this to human bias only and do not engage with the possibility of bias being an inextricable part of the technology itself.

If there is a problem in society, Lee and Chen posit, some form of AI will solve it. And, well, if another problem arises as a result of this intervention, thats the fault of the human developers and users, not the technology. AI, then, despite all of Lees careful explanations of various forms of technological processes, functions as a curiously abstract object it is an object and a concept that seems completely divorced from its social reality. If AI does something good, it is because AI is good; if AI does something bad, no it didnt the user did. The text transports AI from the realm of a socially embedded tool to the realm of conceptual cure to all social ills without recognizing it as a product of those same societies. This is not at all to say that Chen or Lee ignores problems that develop from the use of increasingly sophisticated AI technology, but rather that those problems are blamed on people while successes are attributed to the technology itself.

As a result, AI 2041 can be read as an attempt to validate the expansion of data-exploitation technology of the type represented by Google which, of course, as former head of Google China, Lee is especially prone to rhapsodizing. As a speculative industrial technology, the reification of AI demands the optimization and financialization of everyday operations in the interest of big tech market growth while continuing to insist that this benefits everyone. Yet, it is individual behavior that is called to account for the subsequent problems arising from its use, not the creators of the technology, nor even the technology itself.

In The Golden Elephant, for example, Ganesh Insurance a deep-learning-enabled insurance program optimizes every action and interaction in India to obtain the best premiums for its citizens. But as has been well documented, relying on deep learning algorithms that have been trained on prior demographic data results in a retrenchment of existing social divisions in this case, the caste system. In this particular story, for example, the protagonist is warned away from even talking to her love interest, who lives in a historically lower-caste neighborhood. The Ganesh algorithm used by her familys insurance company has been trained on a corpus of demographic data that conflates data about crime, income, location, etc. to arrive at the objective conclusion that interacting with people from this neighborhood is likely to result in adverse outcomes. The algorithm itself cannot act as an activist to correct for (or even identify in the first place) the redlining policies that have led to this inequality and can only make predictions based on historical data. This type of algorithmic bias has been written about extensively by scholars such as Ruha Benjamin, Safiya Umoja Noble, and Rumman Chowdhury, but Chen and Lee do not address this entrenched bias here except to present it as an attribute of human behavior, not a problem with the technology itself.

Despite a fundamental flaw with the books approach to the role of technology, the collaborative format juxtaposing short stories with a laypersons explanation of AI concepts is interesting for literature fans and futurists alike. One can easily see how this project fits into similar experiments, such as the Center for Science and the Imaginations monthly Future Tense Fiction (which pairs a piece of short fiction about developments in science and technology with a response by experts working in that field) or the work of the futurist forecasting company SciFutures, which employs a rotating stable of SF authors to prototype futures and accelerate innovation (including, most notably, Ken Liu, Chens first translator, mentor, and friend). As a supposed oracle of future development, though, one has to wonder if Chen is writing himself out of the very narrative hes constructing: elsewhere, Lee has developed an algorithm that replicates Chens voice, and the short story The State of Trance (not included in this collection), which incorporates passages generated by the Chen-AI, won first place in a Shanghai literary competition. The competition itself was moderated by an AI that had been trained on the same sort of corpuses and datasets discussed here in AI 2041, and deemed the artificially generated text superior to writing submitted by the Nobel laureate for literature, Mo Yan. Lee has gone on record both in this book and elsewhere to claim that AI is fundamentally incapable of creativity, but if it is capable of reproducing an authors voice so exactly that it is indistinguishable from the real thing, it may very well be that the next collaboration will be between Lee and an AI co-author.

Is all this to say that artificial intelligence, as a concept and as presented in AI 2041 specifically, is bad? No, but neither is it a net good, as Lee posits, or even neutral, as when he writes, Technology is inherently neutral its for people who use it for purposes both good and evil. Artificial intelligence can only do what humans have trained it to do in the way that they have trained it to learn; it cannot be separated from its sociocultural and technological embeddings. As current CEO of Sinovation Ventures and a senior executive at Microsoft, SGI, and Apple, Lee has much to gain financially from the promotion of AI as a set of tools, and much to protect in his assertions that its enormous problems are not inflicted by AI, but by humans who use AI maliciously or carelessly. It is hardly reactionary to be suspicious of the promotion of items that can be developed by and profited off of by companies with investments in education (as we see in the story Twin Sparrows, which mirrors the rise of online surveillance software for education like Proctorio or Microsofts own partner, Pearson VUE), the internet of things (as with several of Sinovations investments, such as Megvii and 4Paradigm, and which is endemic throughout each of these stories), autonomous vehicles (as in AI 2041s The Holy Driver and, in real life, Sinovations investment, Momenta, alongside Googles Waymo), and more.

Perhaps the most egregious of this preciousness toward AI can be seen in how the authors conceptualize risk and harm, as well as the role AI technology plays in mitigating them. Even as Lees own venture capital firm invests in the development of Bitcoin and cryptocurrency supercomputing hardware, he writes in the intro to Quantum Genocide that it is actually AI-enabled autonomous weapons that are the greatest danger from AI. This, maybe more than anything else, illustrates the limited scope of a project aimed at promoting the hugely beneficial [role AI and AI development companies will offer] to humanity [] despite its [human] costs, which can conceive of weapons as threats but not the existential threat of automation in insurance, transportation, and medical decisions that kill far more people in far less flashy ways. Within this same story, Bitcoin a technology we increasingly know has caused environmental degradation at a staggering rate, accelerating climate change worldwide is lauded as a desirable technology even when the exact same story describes climate change as an existential threat to humanity. It is not Lees explanations of the technology which are clear, rigorous, and detailed that are the problem, but his framing of AI technologies as separate from the financial interests behind their development and which posit companies like his own as simply the providers of unproblematic technological solutions.

So then is all this to say that AI 2041, as a text, is bad, as in literature, or bad, as in conceptual ethics? No to the first, but perhaps to the second. Fans of Chens science fiction will find much to love here, but theres something distinctly uncomfortable about reading a collaboration between someone who predicts the future in fiction alongside a representative of major corporate interests who claims to be building such a future. One gets the impression that Chen is attempting to remain neutral in his descriptions of how AI can (and already is) shaping our world even as Lee describes this same technology as, itself, ethically neutral. But in the world we have right now, the one poised on the brink of an AI revolution that is itself the product of human biases and uneven application, who can afford to be neutral?

Virginia L. Conn is a lecturer at Stevens Institute of Technology.

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The Tyranny of Neutrality in AI 2041 - lareviewofbooks

autonomous caregiver robot is holding a insulin syringe, giving it to an senior adult woman in her ... [+] living room, concept ambient assisted living

During the mid-twentieth century, managing the household was transformed by the mainstreaming of technological innovations such as washing machines, dishwashers and vacuum cleaners.

Perhaps in three decades from now, technology will have evolved to a level to allow humanoid robots, such as Andrew played by the late Robin Williams in the 1999 movie Bicentennial Man, to take over the household chores entirely.

Whether or not this represents a flight of fancy, what we know is that technological advancement rarely happens in great leaps but rather, through incremental steps.

Sydney and San Francisco-based AI and robotics startup akin, who have developed robotics alongside the likes of NASA, are currently working towards one such important staging post.

Smart speakers like Amazon Alexa and Google Home are already transforming the home for many and voice activation provides a particular boon for users with physical disabilities.

Akin is focusing on taking things one stage further by creating a more empathetic, goal-orientated home AI system that will be initially targeted towards caregivers living with disabled family members.

Taking on, in its first iteration, the form of a voice-activated kitchen hub with tablet-based personalized breakout avatars for every family member akins system is designed to help with everything from meal planning to setting goals and the efficient allocation of household chores.

Earlier this month the company was named as one of 11 startup winners in the LG NOVA Proto Challenge. The Challenge focused on the key areas of connected health, energizing mobility, smart lifestyle, the metaverse and innovation for impact.

The winners will go on to join a shortlist of 50 or so organizations participating in LG NOVAs Mission For The Future Challenge.

In the meantime, LG will provide mentoring, networking and marketing opportunities in addition to $10,000 in prize money with a chance to pitch for a further $100,000.

Ultimately, those who are successful in the umbrella LG NOVA Global Mission For the Future Challenge will be able to draw on an investment pool of $20 million in equity funding and hopefully enjoy a fruitful partnership with the tech giants U.S. innovation center.

For akin, the attraction is obvious. Developing innovative software is one thing but tech startups often lack the financial heft to venture into the somewhat dicey hardware side of the equation.

In akin's case, related to home hubs, ambient computing and hopefully, further down the line - robotic helpers.

For LG, the feeling is mutual as Dr. Sokwoo Rhee, senior vice president for innovation at LG Electronics and head of LG NOVA explains:

When you develop technology with a clear vision and a clear goal, which is going to bring real value to people, thats when its going to have the highest impact.

Too few companies have this type of vision from the outset while also being fully aligned with the philosophy of our challenge. Thats where we feel akin is different.

Over the long-term, akin is passionate about hosting its sophisticated high-end chatbot inside a robot, whatever shape that might take, to provide physical support.

For now, the priority remains in creating a software interface with the intention of easing the cognitive load on family caregivers. The company is adopting a universal design approach by initially honing in on disability use cases.

Right now, a typical scenario might be that of Emily a girl with cerebral palsy and mild intellectual impairment.

Emilys mom might tell the AI that Emily wants to be more independent but is often late for school because she struggles to pack her school bag every day. She also has to be careful, as the bag cant be too heavy on account of her disability.

This goal will be relayed to Emilys personal avatar on her tablet who she has specially chosen to have long blue hair. The interface has also been rendered with large buttons to help her navigate better.

Emilys avatar talks to her differently from mom when she is rushed off her feet in the morning getting the kids to school. Her assistant will remind her to pack her school bag the night before and be careful not to make it too heavy.

At the same time, Emilys older brother Jack is reminded by his avatar that its his turn to cook the dinner tomorrow night and the meal plan says its going to be vegetable pasta.

The akin interface

Liesl Yearsley is CEO of akin and a world-leading artificial intelligence expert. She has founded three successful companies, including Cognea Artificial Intelligence, which was later acquired by IBM Watson.

Two of her children live with a disability.

The emotional load and level of organizational labor facing families living with a disability are significant, says Yearsley.

Its around 10-15 hours a week. That is the key problem area we are working on with our software. We want to build an ambient, centralized AI that understands the goals of the family and individual, has empathy and can actualize as well.

Yearsley continues, We feel, in these early days, that we can manage much of this organizational load with software but when the technology gets there, we will have a physical assistance mode too.

Across wider society, there exists a rapidly expanding business case for help with managing domestic tasks and family affairs, that may not have existed, or at least not been fully appreciated, in previous decades.

Sometimes, when Ive engaged with the investment community what I hear the most is I just dont get the problem, says Yearsley.

This is because most of them have a chef, a housekeeper, nannies for the kids - a whole entourage that supports their household.But thats not how most of us live. Most people are cutting a 20-40 hour a week job in addition to what has to be done at home.

For the last couple of decades, the economics around this type of innovation might not have worked. But what's happening now is 72% of millennials are working parents and millennials are now the largest living generation.

Today, its not like grandma's time where there is somebody unpaid doing this housework and it not being viewed as an economic cost. We now have this more vocal generation coming through with clearer ideas about the value of their time, says Yearsley.

While the age of the robot butlers or even the super-intelligent Ai voice assistants living in the walls might not yet be upon us, its already a frontier opening up due to the innate desire of us as humans to innovate and explore.

Thats not to forget, the more modern era trend to want to make what we see in sci-fi movies come true in real life.

There are many hard yards to cover before we get there but understanding how to interweave technology into the granular and unique dynamics of individual family units seems like the perfect place to start.

Read more here:

LG And Akin To Develop AI Home Helpers For Families Living With Disability - Forbes

More than 25 years after Microsoft visionary Bill Gates advised the world that content is king, the plain truth is that many people just arent very good at it even the written content they were producing long before the internet arrived. Whether youre creating long-form fiction or focusing on snappy marketing copy, it turns out that writing articulately and engagingly is harder than it looks.

Enter Instoried, which is today announcing the completion of an $8m funding round as it launches its content enhancement platform in the US. Powered by artificial intelligence (AI), Instoried provides instant feedback on the written content created by businesses and individuals, as well as tips for improvements to drive greater engagement from the audience. With just a few words to go on, the tool will even create content automatically on your behalf.

When most of us create content, we just have no idea whether it will resonate with our target audience, reflects Sharmin Ali, who founded Instoried in 2019. We saw an opportunity to help people connect, by assessing the 'empathy quotient' of their content before they publish it the extent to which it will engage the audience.

To achieve that goal, Instoried uses an AI tool built with millions of data points from existing online content. For any given piece of proposed new content, the tool can assess the type and depth of sentiment it is likely to prompt in the audience, measure that against the goals for publishing it, and make recommendations for how to improve the final output before posting.

In theory, the tool could be valuable to anyone publishing any type of written content online. But Instorieds primary target market in the short term is the millions of businesses that create content for their customers, with a view to persuading them to click through and find out more about their products and services.

Around 40 businesses across Asia are already using the platform, Ali says. She points to data suggesting that these clients have seen their return on investment in content more than double during the first six months of using Instoried, with an average 23% improvement in click-through rates.

Im not saying that AI will replace content creators altogether, but our content engine does make life easier for creators that are currently having to create huge volumes of content manually each day, Ali says. Some customers are already using the platform to create suggested content from scratch, while others are using it to perfect their own content creation.

Instoried founder and CEO Sharmin Ali

Ultimately, the success or failure of the Instoried proposition will depend on the quality of its outputs. Ali says the tool is more sophisticated than potential rivals because it can grasp the context of content as well as grappling with its semantics. As an example, she cites an item about the killing of a terrorist, which would appear to have negative connotations given the use of words such as killing; Instorieds engine would rate such content more positively, Ali says, because it would recognise the broader context of a threat removed.

The Instoried business model is changing as the company evolves. Customers were originally asked to pay an upfront fee to permanently acquire the tool, but Ali now sees the potential for a monthly subscription model too. All the more so since Instoried has now begun targeting individuals who are creating content as well as business customers.

Sales are increasing at pace up 50 times over the past 12 months. The companys is now producing revenues worth the equivalent of $1m a year and hopes to be at $2m by the end of its financial year.

That performance, along with Instorieds groundbreaking technology has caught the eye of investors. The companys $8m fund-raising round is led by Pritt Investment Partners and 9Unicorns, with participation from Mumbai Angels, Venture Catalysts Angel Fund and SOSV.

Instoried has successfully developed a SaaS based deep-tech platform that helps enterprises and individuals create emotionally engaging content, thereby improving return on investment for marketing efforts and campaigns, says Scott Tripp, a director at Pritt. As the digital marketing industry continues to grow, we believe Instoried is well positioned with their product to take advantage of this opportunity and quickly penetrate the market.

Alis immediate challenge is to make a breakthrough in the US market. The companys funding will help, with funds earmarked for recruitment in the US, as well as a step-up in marketing, where Instoried has done very little work to date. Ali is also conscious of the need to build out its technology, proving more options for users to plug it into a broad range of content creation devices in multiple channels.

The bottom line is that content creators need all the help they can get to communicate more effectively, Ali argues. It is all about how you connect better with your audience, she says. Does your audience understand what you are selling and is it inspired and excited by what you have to say?

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How Instoried Is Using AI To Turn Your Content Into Something People Actually Want To Read - Forbes