Robotic technology will shape the world for better efficiency.

The Robotic Industry is changing the landscape of work in the modern era. Right from automation in every industry to autonomous surgeries in healthcare, robotics is now becoming capable of handling intricate responsibilities and repetitive tasks and perform them without errors. This is one of the reasons, industries are increasing their dependency on robotics. While this indicates the future of the industry, many organizations and startups are leveraging this demand to create innovative solutions.

Here are the robotic companies that received funding in the month of July to carry on their work and research.

• TOP 10 CLOUD ROBOTICS START-UPS TO KEEP AN EYE ON
• LATEST INNOVATIONS IN ROBOTICS SET TO TRANSFORM INDUSTRY 4.0
• HOW TO INVEST IN ROBOTICS AND ARTIFICIAL INTELLIGENCE

1. Realtime Robotics

Realtime Robotics is an autonomous industrial robotics company. The company offers real-time, collision-free motion planning that enables users to leverage robotic control technology to navigate applications. With just minimal programming, users can control the functioning of robots across multiple deployments. Realtime Robotics raised US$31.4 million in its Series A round. The funding was led by HAHN Automation for the purpose of improving robotic capabilities.

2. Verve Motion

Verve Motion pioneers connected wearable technology for the industrial sector. It accompanies robotics into functional apparel and boosts worker safety and wellbeing with their technology. The company recently announced that it has raised US$15 million in Series A funding which was led by Construct Capital along with Founder Collective, Pillar VC, Safar Partners, OUP, and other angel investors.

3. Seoul Robotics

Seoul Robotics is a 3D computer vision company that innovates intelligent robotic perception systems using artificial intelligence and machine learning. The company’s first commercial model, Discovery, made its debut in the US market, earlier this year. It is an all-in-one sensor and software platform that expands access, reduces costs, and simplifies the implementation of LiDAR-powered solutions in several applications for smart cities, smart, factories, and logistic mobility. Seoul Robotics was selected as an Innovative Icon startup for all its efforts and received a US$12 million investment from the Korean Government.

4. Fetch Robotics

Not so much of an investment, but a business firm, Zebra Technologies is planning to acquire Fetch Robotics, a cloud-driven autonomous mobile robot startup. This acquisition aims to boost Zebra Technologies Enterprise Asset Intelligence vision and growth in industrial automation. This will align with Zebra Technology’s focus on robotics and change the infrastructure of the industry with seamless integration.

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Check out 10 top rising cloud robotics start-ups

Cloud robotics is an area of robotics that attempts to entreat cloud technologies such as cloud computing, cloud storage, and other internet technologies. It is not an easy task to run a cloud robotics start-ups. But it is amazing to see the rising number of start-ups specializing in cloud robotics. These start-ups are seen to work on innovative technologies.

Here is the list of 10 cloud robotics start-ups that are working on the innovation of technologies and products.

Graphcore

It was founded in 2016. The company is situated in the United Kingdom. The company’s IPU accelerators and popular software together make the fastest and most ductile platform for current and future machine intelligence applications, which lowers the cost of AI in the cloud and datacentre, improves performance and efficiency by between 10x to 100x. Graphcore systems shine at both training and reasoning. The highly collateral computational resources along with graph software tools and libraries enable researchers to explore machine intelligence across a much wider front than current solutions. This technology lets recent success in deep learning evolve rapidly towards useful, general artificial intelligence.

Arctoris

The company was founded in 2016 and is situated in the United Kingdom. Arctoris serves academic research groups and biotech companies, enabling scientists to directly configure a broad range of cancer research experiments online, and collects their results and detailed methods report instantly. It is not only effective for screening but is created for fundamental research, surrounding a wide range of techniques to cover cellular and molecular biology. Arctoris enables researchers and entrepreneurs to create and implement cutting-edge cellular and molecular biology research experiments and offers a complete system to recognize, analyze and visualize research data.

INVOLI

The company was founded in 2017, Switzerland. INVOLI serves in building infrastructure that enables drone revolution, ensuring their safe consolidation into today’s aviation world and avoiding collisions with aircraft. It offers an online platform, which collects data from a network of scaled-down control towers gathering maximum information on the position of every flying object in the sky, manually or automatically. Its technology enables everybody to see the air traffic encompassing them and take the most appropriate measures. Thus, what INVOLI brings to the aviation world is intelligent air traffic instruments and automatic collision avoidance tools, allowing low-risk flight plans and avoidance strategies and thus creating a safer, smarter sky for all.

Salty Cloud, PBC (Public Benefit Company)

It was founded in March 2017 in, USA. The company offers next-generation security automation technology to assist organizations around the globe to protect their cyber adversaries. Today, the company provides three security & risk automation tools to the public. It continues to find ways to provide free versions of its tools for educational institutions, state & local governments, and non-profits. One such offering is Dorkbot, the web vulnerability scanning service.

Bright Machines

It was founded in 2018 in, USA. The company aims at making manufacturing smarter and more efficient. It merges intelligent software with flexible factory robots and machine learning to help the customers deliver the next-generation products

Soar Robotics

The company offers independent operations at scale for urban and industrial applications with seamless connectivity and cloud intelligence. It uses hyper-realistic visual and physical simulation environments to safely test hardware by recreating real-life scenarios in virtual worlds. It helps in executing custom applications for proprietary use cases.

Freedom Robotics

It was founded in 2018, headquartered in San Francisco. Freedom Robotics integrates development and management tools in its cloud-based robotics management software (RMS) to help in prototype, creating, operating, and scaling robot fleets.

Covariant

It was founded in 2017, headquartered in Berkeley, Calif.

Covariant is creating AI called the “Covariant Brain” to make robots smarter. Covariant is competing directly with other cloud robotics start-ups such as Kindred and RightHand Robotics. Its technique is to use a variety of AI methods to train its robots, including reinforcement learning. It merges that with an off-the-shelf robot arm, a suction gripper, and a simple 2D camera system.

Aitomation – Workflow Automation

This company leverages robots (physically and virtually) for automating specific tasks. It utilizes the power of cloud computing and process automation to create and modify cloud robotic solutions. The company develops software robotics solutions, lean strategies, artificial intelligence (AI), and low-code applications, as well as cloud services. It allows the automation of long workflows and processes that run across multiple systems and organizational extremities.

RosHub – Robotic Fleet Management

It is a US-based start-up. It develops connectivity solutions for robot fleets. The company is also involved in prototyping, testing, and applying robotic solutions in unstructured public spaces. It allows the link of an open-source ROS (robotics operating system) that is combined efficiently with the support of existing tools and APIs.

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In 2013, Google started a pioneering and ostentatious effort to manufacture robots. With time, its target has become self-effacing but with time the technology is also subtly more advanced. Since 2013, the internet company has depleted tens of millions of dollars in buying six robotics start-ups in Japan and in the United States. The project consisted of two teams that are majoring in machines that moved and looked like humans. Andy Rubin, the Vice President of engineering who was behind the effort of creating such projects named it Replicant in a nod to Google’s great ambition. Also, the name was used in a science-fiction movie called ‘Blade Runner’.

However, with the passing time over the next few years, Google sold off the companies that have been acquired by it or shut them down. The Japanese conglomerate SoftBank bought the best-known project named Boston Dynamics from Google.

In recent years, with the advancement of modern technology, Google accumulated and reassessed its target on the mechanics of complex robots. For the last few years, Google has been remodeling its program focusing on robots that are much more manageable and simpler than human-shaped machines.

Vincent Vanhoucke, who previously helped Google in building Google Brain that researches artificial intelligence is now leading the new robotics at Google. This new effort is called robotics at Google. It includes many of the engineers and researchers who worked under Mr. Rubin. Its new model will be able to learn skills independently using machine learning without human intervention, like traversing in a warehouse that is filled with unexpected objects. The machines may not impressive or attractive as the earlier humanoid robots but this more advanced technology incorporated inside them gives them more perspective in the real world.

In the warehouse and on factory floors, Robots are already in use but they can only operate certain specific tasks only like turning screws or picking up objects. Taking the help of machine learning Google wants the machines with which it is working to learn on their own.

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DataRobot brengt enterprise AI-platform naar Nederlandse markt om organisaties te helpen sneller waarde uit data te halen.

DataRobot, leverancier van een geavanceerd enterprise AI-platform, betreedt de Nederlandse markt en opent een kantoor in Amsterdam. Het platform, met end-to-end automatisering voor het bouwen, implementeren en beheren van machine learning-modellen, maakt data science breder toegankelijk. Bedrijven kunnen zo eenvoudiger AI-modellen bouwen en in gebruik nemen. In Nederland tekenden de Gemeente Den Haag en de Nierstichting als eerste voor het platform van DataRobot.  

De introductie in Nederland is een logisch gevolg van de enorme expansie van DataRobot. Het Amerikaanse bedrijf is opgericht in 2012 en is sindsdien exponentieel gegroeid. DataRobot heeft de afgelopen jaren verschillende bedrijven overgenomen, zoals Nutonian, Nexosis, Cursor, ParallelM en Paxata. Sinds de start is er meer dan $750 miljoen in het bedrijf geïnvesteerd. DataRobot wordt inmiddels gewaardeerd op $2,8 miljard en werkt voor veel Fortune 500-ondernemingen. Inmiddels zijn er meer dan twee miljard machine learning-modellen gebouwd op het cloud-platform. Naast het openen van een kantoor in Nederland is DataRobot onder meer actief in de Verenigde Staten, het Verenigd Koninkrijk, Denemarken, Oekraïne, Singapore en Japan. 

“Voorheen vergde machine learning veel tijd en vereiste het veel handmatig programmeren. Daardoor konden organisaties slechts ten dele van de technologie profiteren”, vertelt Joep Gerrits, Regional Sales Manager bij DataRobot. “Er is een tekort aan data scientists, en zonder hen zijn bedrijven beperkt in het aantal modellen dat ze kunnen ontwikkelen en testen. Bovendien is het naar productie brengen van de modellen vaak een issue, omdat de data scientists en degenen die de modellen moeten toepassen niet dezelfde expertise hebben. Dit betekent in veel gevallen dat modellen soms al zijn verouderd tegen de tijd dat ze ingezet zouden moeten worden.”

Integratie kennis data scientists

Om dit probleem op te lossen, integreert DataRobot de kennis van meer dan 400 van ‘s werelds beste data scientists en engineers die het in dienst heeft, in zijn platform. DataRobot beschikt over een uitgebreid arsenaal krachtige, open-source algoritmen en technieken voor machine learning. Deze worden continu up-to-date gehouden, zodat iedere klant zonder uitgebreide ervaring met data science of codeer kennis de best passende AI-oplossing kan kiezen en inzetten.   

Gerrits ziet door de krachtige technologie van DataRobot veel kansen om het succes verder voort te zetten op de Nederlandse markt. Om deze te benutten wordt de lokale organisatie snel uitgebreid. “Organisaties zijn niet alleen op zoek naar een manier om sneller en makkelijker modellen te bouwen. Ze willen deze ook naar productie kunnen brengen zodat ze daadwerkelijk waarde opleveren. Wij helpen hen bij beide en hebben daarmee een sterke propositie om ook in Europa snel te groeien.” 

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DataRobot’s AI platform makes it easy to build predictive models to identify infection hotspots

A new data-driven project is launching with the ambitious aim of resolving the COVID-19 pandemic in 60 days. 

ContagionNET has been designed by DataRobot, a global enterprise AI company that enables easy building and deployment of predictive models. Aimed at solving the data sharing problem that has plagued the COVID-19 pandemic response, the not-for-profit initiative will help determine when a patient is at their most contagious.

ContagionNET combines affordable antigen checks performed at home, anonymous data collection, and an AI platform. With these tools DataRobot are aiming to identify the most contagious areas in a community and encourage people to take preventative action. The goal is to identify people earlier than traditional testing (i.e., when they are most infectious) to disrupt the chain of transmission. 

ContagionNET can also predict how many people need to participate on a county-by-county basis by measuring the impact of different participation levels and testing frequency. This localised approach allows those with the highest viral load to self-isolate earlier, preventing further spread of the virus. 

“The reality is that those most likely to spread COVID-19 aren’t usually aware they are contagious” said Sally Embrey, DataRobot’s VP of Public Health and Health Technologies. 

“With the proliferation of frequent, easy-to-use at home antigen checks, ContagionNET is poised to inform the most contagious of their risk and help them make lifestyle modifications that will significantly reduce spread of the virus.”

DataRobot has been involved in pandemic response since early 2020. Working with the US government, DataRobot’s forecasting helped vaccine manufacturers select the right participants and prioritise enrollment in the highest risk locations over each vaccine trial period. 

The organisation has also used modeling to improve testing distribution, equity, and diagnostic reporting, all of which have informed its approach to ContagionNET. 

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How to bridge the production gap between data and IT

Data scientist has been one of the superstar IT roles of recent years, with the promise of spinning data into gold with the application of AI and machine learning.

Using cutting-edge technology to extract deep insights from reams of business data, data scientists aim to help guide their organisations into a more innovative, efficient and profitable future. But so far, return on investment hasn’t always been what companies might hope.

“One of the biggest mysteries in data science today actually has very little to do with data science: What is that last mile to AI ROI?” says Sivan Metzger, managing director MLOps and governance at DataRobot. “You build your machine learning, you find the data, you get it cleaned up, you build the models, you try 90 different iterations, you make a good and clean one and it’s ready to go. What happens then? Why are we not seeing value at scale from AI?”

Metzger credits these issues to a disconnect between the data team, IT operations and stakeholders on the business side (i.e. the potential consumers of data science insights). Data science and IT operations teams have very different considerations and goals – and machine learning is very different from running software. This disconnect is known as the ‘production gap’, and can prevent AI solutions from being properly executed.

Machine Learning Operations (MLOps) is a combination of processes, best practices and underpinning technologies which seeks to bridge this gap by increasing collaboration and communication between data scientists and operations staff – and ultimately ensuring that AI is properly deployed and can begin to deliver the ROI promised.

To learn more about how MLOps can improve your returns on AI, watch IT Pro and DataRobot’s webinar ‘The Last Mile to AI ROI’, in which Metzger and data scientist Rajiv Shah discuss topics including:

• How to eliminate AI-related risks by adopting MLOps best practices
• The inherent challenges of production model deployment and how to overcome them
• Model-monitoring best practices
• Production lifecycle management and why it matters

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BOSTON–(BUSINESS WIRE)–AtScale, the leading provider of intelligent data virtualization, today announced a partnership with DataRobot, the leading enterprise AI platform, to deliver a turnkey approach to predictive and descriptive data analysis. The partnership builds a bridge between data science and business intelligence to incorporate and share the same KPIs used for predictive and prescriptive business decisions.

“We are thrilled to partner with DataRobot to accelerate our go-to-market within the data science community. DataRobot’s vision for maximizing business value via AI is unparalleled, and the combination of our capabilities is another step toward democratization of predictive and prescriptive analytics,” said Christopher Lynch, executive chairman and CEO, AtScale.

Organizations globally are moving AI predictions into day-to-day business operations. The combination of DataRobot and AtScale provides a consumable enterprise metrics hub with consistent, governed KPIs and a simplified interface for automated feature creation utilizing DataRobot’s Feature Importance and Impact functionality. AtScale uniquely makes DataRobot predictions consumable to business intelligence and reporting workstreams, providing both technical and non-technical stakeholders access to predictions using the tool of their choice.

“Our global market presence and diverse customer base continue to highlight the need for greater unity between the data science and business intelligence communities,” said Jeremy Achin, co-founder and CEO, DataRobot. “AtScale’s intelligent data virtualization adds a new business constituency in the enterprise who can incorporate AI predictions for operational decision making.”

About AtScale
AtScale powers the analysis used by the Global 2000 to make million dollar business decisions. The company’s Intelligent Data Virtualization platform provides organizations the opportunity to enhance or create a self service data culture by leveraging its business-friendly semantic layer, intelligent data engineering, easy-to-use web-based design interface, multi-cloud support, and strong data source and client integration.

Learn more about why AtScale is a leader for enterprises to deliver fast, accurate data-driven business intelligence and machine learning analysis at scale in GigaOm’s Data Virtualization Radar Report. For more information, visit www.atscale.com, and connect with us on Twitter, and LinkedIn.

About DataRobot
DataRobot is the leader in enterprise AI, delivering trusted AI technology and enablement services to global enterprises competing in today’s Intelligence Revolution. DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting-edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

DataRobot has offices across the globe and funding from some of the world’s best investing firms including Alliance Bernstein, Altimeter, B Capital Group, Cisco, Citi Ventures, ClearBridge, DFJ Growth, Geodesic Capital, Glynn Capital, Intel Capital, Meritech, NEA, Salesforce Ventures, Sands Capital, Sapphire Ventures, Silver Lake Waterman, Snowflake Ventures, Tiger Global, T. Rowe Price, and World Innovation Lab. DataRobot was named to the Forbes 2020 Cloud 100 list and the Forbes 2019 and 2020 Most Promising AI Companies lists, and was named a Leader in the IDC MarketScape: Worldwide Advanced Machine Learning Software Platforms Vendor Assessment. For more information visit www.datarobot.com, and join the conversation on the DataRobot Community, More Intelligent Tomorrow podcast, Twitter, and LinkedIn.

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I’m writing this just before New Year’s Day, and like many of you I’m hoping to reach my one remaining goal for 2020, which is to be alive in 2021. I’m looking forward to putting 2020 behind, and while the pandemic has been painful, it has caused things to accelerate in several areas impressively rapidly. Two of those areas are robotics and artificial intelligence, which we’ll see adapted broadly this decade with a considerable bump in 2021.

Let’s talk about all of that this week, and we’ll close with my first product of the week in 2021, the Somnofy AI Sleep Monitor.

Robots, robots, and more robots will increasingly surround us in 2021.

We ended 2020 watching the Boston Dynamics robots do choreography. This showcased just how incredibly capable these things are becoming and making some of us wonder if we should be looking for Skynet.

However, rather than anticipating robots that will either kill or protect us, much of what we’ll see is delivery and food prep.

One of the big things that happened late in 2020 was that the FAA cleared the way for drone delivery, allowing drones to fly over populated areas and at night. Initially, drone delivery will mostly deploy in rural areas where it makes sense given the lack of high-density housing such apartment complexes that don’t lend themselves to drone delivery. Still, for some of us, drones delivering packages will be common by the end of this year. For folks in New York and Las Vegas, Walmart will be delivered by air to you shortly.

We aren’t just talking about flying drones either, as drones with wheels are coming off their 2019 trials and going into production. California is the first state with large delivery autonomous vehicles. However, I expect, primarily because we are still mostly home during the days now — so we are around to unload the things — that this will spread far and wide, again mostly in rural areas, by year’s end.

But this won’t just be for the delivery because restaurants are becoming automated as well, since machines don’t catch or spread diseases and can operate 24/7. It strikes me that we could have gone back to the automat concept to separate customers from those preparing and serving the food. Still, the automated restaurant appears to be blending industrial robots for food preparation and delivery.

Automation deals with two problems: the lack of people that want to work in fast-food restaurants (virtually all of them in my area have a help wanted sign) and keeping the place open during a pandemic.

Finally, we are short hundreds of thousands of truckers, and one of the ways to address this is to put autonomous tractor-trailer rigs onto the major highways for long-haul routes. This outcome is iffy because the unions are fighting this. Still, given the massive shortage of drivers, shipping companies’ only path is to replace the drivers, which means it won’t be long until semi-trucks no longer have drivers.

AI Domination

Another place we are going to see a ton of deployments is AI. From temperature monitors at entrances to transportation, offices, hospitals, and restaurants that alert and sometimes prevent entry for those running a bit hot, to radically improved digital assistants, we are going to be surrounded by AI.

One of the exciting efforts that started in 2020 was Nvidia’s Broadcast app and its Omniverse Machinima.

These showcase how an AI can significantly enhance your next video conference. Think of GPU-centric capabilities using technology like deepfakes to give you a desirable looking virtual office that looks more realistic than the backgrounds we currently have, the elimination of background noise, and even the ability to take years off your age and dress you virtually so you appear more professional on screen.

In the following years, I expect this ability to dynamically clothe you will advance so you will appear to be dressed in culturally acceptable ways for each country you are broadcasting. I expect we’ll also have issues that the movie “Lost in Space” showcased when Will Robinson replaced his teacher’s image complaining about his behavior with a cartoon ape. I can think of several ways this capability could end badly for someone.

With your digital assistant integrating with products like the Cisco Webex Desk Hub blending your office and smartphone and backing it up with an office-focused digital assistant, we’ll have Alexa-like capabilities in our homes and offices.

These won’t be like Siri but are increasingly backed by mature AI technologies like IBM’s Watson to provide far more profound insights and better answers to your business questions. It will be easier to find out who is responsible for something, whom you need approvals from, whom you should be collaborating with, and get far more timely alerts if you are doing something that could land you in trouble.

Finally, the AI-driven health monitors that we wear are due for a substantial upgrade. Think more accurate blood pressure, insulin, and heart monitors tied directly to EMTs and medical services so that you get the medical help you need more quickly and more accurately. This advancement should be the beginning of embedded technology and form a bridge to advanced prosthetics. These advancements include improved brain interfaces, which will also lay a foundation for future man-machine interfaces.

Wrapping Up

I’m only touching on a few of the advancements we’ll see in 2021, but at the heart of most of these are now-maturing advancements in man-machine interfaces, more capable AI systems, and increasingly prevalent robots and autonomous vehicles.

Oh, flying cars, flying suits, and flying motorcycles are also moving into production in 2021.

These emerging technologies should make 2021 a fascinating year — though it will likely take much of the decade to make all of this new technology acceptable to most of us.

The Somnofy Sleep Monitor

If you are like me, getting a good night’s sleep during this pandemic is problematic. One of the best products I’ve so far found is the AI-driven Somnofy Sleep Monitor.

Unlike other products I’ve tried, there is little setup, and you don’t have to find the right place under your mattress to put some screwy sensor that often doesn’t work. The Somnofy Sleep Monitor uses non-contact sensors and a deep learning AI to monitor and report on the quality of your sleep, your sleep time, sleep efficiency, and help you eliminate the things that are preventing you from getting a good night’s sleep.

The Somnofy measures sleep quality, respiratory rate, sleep stages, the air quality in your room, ambient light, light spectrum (blue light inhibits sleep), ambient noise, and room temperature to provide a detailed report quality of your sleeping.

It also monitors your vital signs to alert you if you have a breathing problem before the issue becomes critical. Created in Norway, one of the countries that takes personal health more seriously than most, Somnofy will even provide you with techniques like guided breathing to help you fall asleep. However, it will not monitor two people in the same bed without getting a second unit as it focuses on the person closest to the monitor.

Finally, the unit has a smart alarm function that waits until you are in light sleep to wake you up, so you feel more refreshed upon rising. Since the Somnofy sleep monitor is an example of one of the new applied AI capabilities coming to market (I gave it to my wife for Christmas), it is my first product of the week in 2021. 

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Could giving robots voice help them learn human commands?

Robots have become an integral part of human’s daily lives. They help us in numerous ways, from performing complex tasks to lifting heavy weights and assisting the elderly, playing with kids, and entertaining people at events. They can interact with people in any scenario. However, construing a human language still a challenge for robotic systems. Training them with real-world experiences and knowledge about the world could help robots understand natural language.

People use language to express emotions, direct behavior, ask and answer questions, provide information, and ask for help. Language-based interfaces for robots require minimal user training and expression of a variety of complex tasks.

In a paper, researchers from MIT describes a new way to train machines. They noted that children learn language by observing their environment, listening to the people around them, and understanding what they see and hear. With keeping that in mind, they created a tool called semantic parser that mimics the experience of children learning a language. Parsers are already being used for web searches, natural-language database querying, and voice assistants. The system observes captioned videos and links the words that speakers say with recorded objects and actions.

As parsers are trained in sentences annotated by humans, they could be used to improve natural interaction between humans and robots. According to the paper, a robot equipped with the parser could observe its environment to reinforce its understanding of spoken commands, even when the spoken sentences are not fully grammatical or clear.

Earlier, Analytics Insight reported that how giving voice to robots within healthcare influence human perception. Already, robots are delivering a wide range of healthcare services and opportunities to medical personnel and advancing patient care delivery. In this article, we noted how researchers at the University of Auckland and Singapore University of Technology & Design have been using speech synthesis techniques to create robots that sound more empathetic. As part of their study, researchers tested a hypothesis on how a robot’s voice can impact users’ understanding by conducting a simple experiment using a robot called Healthbot. They used a professional voice artist for the robot’s voice, which was recorded while reading dialogs in two voice variations: a flat monotone and an empathetic voice.

More broadly, teaching a machine to speak and making them able to recognize human voice is a crucial yet effective step as spoken language is the most intuitive form of interaction for humans. In 2018, it was reported that researchers in Japan attempted to bring audition, or power of listening, to robots. Proposed by Tokyo Institute of Technology Professor Kazuhiro Nakadai and Professor Hiroshi G. Okuno of Waseda University in 2000, “Robot Audition” is a research area. For this, they turned their research public and made it open-source software. This essentially helped them generate interest and diversified the research. Their research was officially registered in the IEEE Robotics and Automation Society.

So, when robots and robotics systems are able to learn and recognize the human language, they will have a more emphatic impact on people’s lives.

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Artificial intelligence and robotics are bringing drastic changes in the technological fields. Things we only imagined twenty years back have now become a reality. From automated systems at a manufacturing plant to self-serving robots in a restaurant, technology has evolved, driving humans together. In Today’s world, AI and robots serve people as problem-solvers, companions, and first-responders. Basically, when you chat online with a business on their website thinking that you are talking to their customer representative, you are actually talking to a chatbot. Technology has evolved for good, and it is not going to stop here. 

AI and robotics are getting used in multiple fields

When we talk about AI and robotics, they are not specific to a certain industry. Their adaptability has made them the favorites in all the industries and sectors you can name or think of. From gaming to defense, healthcare, automotive, fitness, education, retail, manufacturing, and whatnot. Online gambling, for example, is a billion-dollar industry, and online gambling platforms like True Blue Casino have already started using AI-based algorithms that control the outcome of the gameplay. 

So, it is safe to say that machines and computers will positively manage most of our dealings. It is just the start. AI, machine learning, and robotics are bound to progress further in the coming years before they become commonplace. Data has played a crucial role in the development of these systems because data has enabled these machines to learn on their own. With that being said, let’s discuss the applications of AI and robotics and how they are going to shape our future. 

Where AI and Robots are used Today? 

AI and robots are a powerful combination for automating tasks. In recent times, artificial intelligence has become a significantly common presence in robotic solutions, bringing in learning capabilities and flexibility in previously rigid applications. While still being nascent, both technologies work well when combined. 

1. Virtual Assistant and Chatbots 

Virtual assistants and chatbots propel the world with astounding automation levels, driving costs down, and productivity. Virtual assistants are a manifestation of AI and machine learning through the simulation of conversation with humans. Virtual assistants and chatbots are designed to obey automated rules using capabilities called Natural Language Processing (NLP). The recent advancements in technology have significantly improved their performance. From Siri to Google Assistant and Alexa, they are the glorified versions of virtual assistants. 

From answering the basic questions like Today’s date and weather to performing some complex tasks like “Hey Siri! Set up an alarm for 8 AM,” these virtual assistants will slowly replace your human assistants. The best part is that they amalgamate very well with machines in your home. With the likes of IoT (Internet of Things), you can command your virtual assistant to turn on the light or AC or music in your house.

2. Agriculture and Farming 

Believe it or not, robotics and AI are your next best bet for sustainable agriculture. With the food supply chain facing a crisis, courtesy of centuries of environmental abuse, over-farming, labor shortages, and population growth, it is threatening our most basic needs. AI and automation are believed to provide relief from the effects of an aging agricultural workforce. With the likes of autonomous drones, self-driving agricultural machines, etc., farmers can spend more time focusing on creating sustainable harvests and less time watching the path in front of them. 

Deere is a well-known agriculture equipment manufacturer that is popular for its self-driving machinery. Also, it expanded its agricultural arsenal with the introduction of an automated weed sprayer. It uses next-gen technology with advanced robotics, machine learning, and computer vision to distinguish between crop and weed. Also, Big Data is helping farmers to deliver better crops. Big Data has given rise to prescription agriculture that uses web-based tools for creating maps or prescriptions, telling farmers how much fertilizer they need to apply to certain crops and areas. 

3. Autonomous Flying 

Autonomous flying uses computer vision technology for hovering in the air while avoiding obstacles and moving in a straight path. With the introduction of artificial intelligence, these flying machines are getting smarter. From aerial view monitoring to security surveillance, video recording, rescue missions, and more, drones and unmanned aerial vehicles are revolutionizing and replacing many job roles. The application of computer vision in autonomous flying includes obstacle detection, collision avoidance, self-navigation, and object tracking. 

Machine learning can bring some drastic changes to how autonomous flying vehicles function. While object tracking UAVs capture real-time data, it also uses an on-board intelligence system that enables it to make human-independent decisions based on the real-time data. 

These drones can be used in urban management and smart cities for advanced surveillance, quick facial recognition, or tracing unwanted objects. They are also highly beneficial in agriculture and farming as they can monitor crops, check the soil fertility, assess soil, and help crop production. Other applications may include: 

• Scanning or mapping terrain of buildings in real estate; 
• In the military to bombard or combat enemies in the war; 
• For human tracking and face recognition. 

4. Retail, Shopping and Fashion 

The retail sector is reaping the benefits of AI and machine learning for some time now. Artificial intelligence is helping retailers better understand their target market through data analysis. Since data is the new currency of this digital world, it can make or break a business. Keeping this in mind, retailers are using predictive analytics to help forecast customer behavior based on sales data. E-commerce sites are using recommendations based on the customer’s regional search trends, location, and search history. Moreover, shopping sites like Amazon offer its customers product recommendations based on past sales data. 

AI also helps retailers enhance their online store by customizing messages they send to their prospective customers. Content generation is a tedious process, but with AI’s Natural Language Generation (NLG), retailers can send targeted messages and offers to customers. 

Robots have been introduced to manage the inventory and sales floor, giving ultimate precision and cutting high costs. And when it comes to fashion, AI is slowly taking over the supply chain and fashion store. From sorting of dresses to sewing, these mundane tasks are performed by AI-induced systems with better accuracy and faster speed. Robots can easily stitch fabrics with precision and can also detect flaws in the material, ensuring quality assurance. 

5. Security and Surveillance 

The robots developed Today use artificial intelligence, long-range sensors, high-definition cameras, and fast computer processing, all of which makes for a pretty decent security system for different needs. Experts believe that robots can easily guard a designated area. There are robots in the making that uses mapping software for creating a geo-fenced perimeter. 

They are designed to monitor the grounds and inside of the building. These security robots are intelligently designed and use differential GPS that can easily find objects within a few centimeters. So, when it is moving, it knows exactly where it is. They can record and store data on a daily basis with their security camera. The foundation of an AI-based security system is a self-monitoring system that features an HD camera. 

The latest AI-powered security robots use facial recognition to store the identities of people visiting a particular house or building and create a catalog of individuals who are regular visitors or those who are known. 

6. Sports Analytics and Activities 

The sports industry is embracing artificial intelligence and robots to make games more exciting and fairer. Sports are more than just games for millions of people. For some, it is an emotion. Above all, it is a billion-dollar industry. With so much at stake, organizations and associations across the globe are trying their best to gain a competitive advantage and keep the fans happy using robotics technology and artificial intelligence. 

AI is helping players improve their fitness and help teams discover new talents. In some sports, robot referees are already a thing, while smart machines are assisting spectators in finding their seats at the stadium. For those who don’t want to visit the jam packed stadium to have fun, their fan experience is retained and redefined using VR headsets. Artificial intelligence is also helping clubs and teams come up with strategies based on previous data. 

The following are some of the interventions that are being implemented in the sports industry: 

• Smart apps and Virtual Reality tech are driving fan engagement; 
• Tech-powered refereeing is soon going to become a reality; 
• Smart algorithms are developing new games; 
• AI is helping team management and support staff to find new star players;
• AI is assisting clubs and teams to protect the wellbeing of their players. 

7. Manufacturing and Production 

The evolution of the manufacturing and production industry is seen with the implementation of robotics and AI. The primary reason for the introduction of AI in the manufacturing industry is to cover for the lack of workforce, simplify the whole production process, and improve efficiency. Earlier, it used to take a whole team’s effort to manage one task system. Now since bots have taken over, it has helped manufacturers boost production speed. 

AI is helping the industry by making product decisions instant and smarter. This is an era of customized products, and AI is helping manufacturers gather useful customer data, which is used to make product-based decisions. Also, it has helped the companies to reduce the overall cost of production. AI and robotics is the future of manufacturing. To get a better understanding of how essential are robotics and AI in the manufacturing industry, have a look at their use cases: 

• Demand-based production; 
• Automatic control; 
• Damage control and quick maintenance; 
• Product design and redesign.

8. Gaming 

Robotics and AI have influenced the way computer games are designed and played. AI is helping game developers to create characters and generate their behavior to imitate humans. The primary goal of artificial intelligence in games is collecting and processing data obtained from players. Above all, it has enabled game developers to create games based on their needs and expectations. 

Also, online gambling has benefited a lot from artificial intelligence. It studies the expectations and preferences of the gambler for example in top 10 online casinos Australia, giving them maximum satisfaction from the comfort of their home. The adaptability and learning nature of the algorithm of AI allows for creating realistic and natural game environments. 

Last but not least, AI-based games have tremendous graphics. It needs a team of hundreds of developers to create such stunning graphics, but thanks to AI, the whole process is automated. This not only saves time, money, and resources as well. 

Conclusion 

Artificial intelligence and robotics are the driving force of the future. In the next decade, you will surely see some stunning technological revelations based on AI. AI is all about data, and when properly implemented, it will use the given data to our benefit, automating most of the processes and making our lives easier. 

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