Why This Delhi-Based Startup Prides Itself As The McDonalds Of Geospatial World

The synergistic integration of artificial intelligence (AI) and the geographic information dimension creates geospatial artificial intelligence (GeoAI).

The synergistic integration of artificial intelligence (AI) and the geographic information dimension creates geospatial artificial intelligence (GeoAI). Geo-tagged big data collated from varied sources, such as satellite imagery via remote sensing, IoT sensors in smart cities, social media streaming, and personal sensing via connected ambient and wearable sensors, can be analysed using GeoAI to get actionable insights.

Founded in 2017, Attentive AI was started by an IIT Delhi core team comprising of Shiva Dhawan, Utkarsh Sharma and Sarthak Vijay. The company was established to develop artificially intelligent systems that can analyse petabytes of geospatial imagery and convert it into accurate insights. It serves geospatial technology providers and end-users with 2D and 3D vector data extracted from satellite, aerial, street and drone imagery.

The mission of Attentive AI is to convert all the data collected by satellites, aeroplanes and drones into actionable insights, and help businesses, governments and non-profit organizations in reaching meaningful conclusions and making better decisions faster.

Every startup has its challenges and everyone should be prepared to tackle those, said Shiva Dhawan, founder and CEO of Attentive AI. “The biggest challenge for any startup is running out of money. Some run out of funding whereas some run out of paying customers,” said Dhawan.

He further said, “From the very beginning, we knew that we would face the latter problem rather than the former. However, we were also confident that overcoming this problem would lead us to build a sustainable business, and so we relied on our customers to sustain ourselves as well as to grow. As a result, we tried to be as frugal as possible with our expenses. For instance, for a long time, we did not work out of a high-end corporate office instead, we worked out of a couple of apartments which were converted into modest office spaces.”


The key to success is to build a team that understands the importance of frugality and enjoy the startup journey just as much as you do, added Dhawan.

Attentive AI’s approach involves experienced computer vision team that prepares deep learning models and are trained on geospatial imagery data, in-house annotators process machine-generated data which fix gaps and inaccuracies, expert in-house quality control team that ensures nearly 100% correctness through pain-staking visual inspection, and client monitors live production until the map features are delivered in the desired file format.

Standing Out Of The Crowd

With the Chinese government leading the AI race with their expansive surveillance system and heavy investment in AI research and skill development, the Asian governments still lag in terms of implementing AI initiatives.

With such a scene in hand, Attentive AI has come up with an innovative solution to deliver the best GIS solutions using high-quality digital maps. MapX is Attentive AI’s flagship product that can be used to request meaningful insights from geospatial imagery data. The core of the product envelops a simple workflow allowing each user to select an address or an area, followed by requesting the analytics they desire from a list of available analytical options or can even go for a custom request followed by near-instant delivery of output/insights.

MapX prides itself on being the ‘McDonalds’ of the geospatial world. Thus, one does not have to wait a long time to get the geospatial dataset or a digital map. “Initially, when McDonald’s had started, people said it was impossible to deliver high-quality burgers in such a short time but they did it, and their key to success was their engineering process. Similarly, delivering high-quality land features almost instantly was also impossible, but Attentive AI makes it possible by an engineering process at the core of which is our AI algorithm supported by a detailed QC process,” said Dhawan.

Being a customer serving web platform, MapX also makes the experiences of ordering geospatial data very seamless for customers and users. “Our customers attest to the seamless geospatial data ecosystem that we are creating.”

Surviving Industry Challenges

AI, being one of the newest innovative technology with a maximum number of commercial benefits, comes up with several industry challenges. The major limitation in the AI industry is customer awareness, or the whole concept of AI, where it is to be believed that AI itself is the solution to all problems. However, that is not true as AI technology alone can not provide a complete solution. AI does aim to solve the most complex steps, but the whole solution can only be created with a combination of multiple technologies including AI.

Explaining this, Dhawan said, “One of the major challenges of being an AI service provider is that a customer’s expectations. Usually, customers tend to have high expectations from AI rather than what is currently possible. And, this is mostly because of the increased hype around AI and also because a lot of companies project a higher AI capability than what is possible at the current stage.”

“These companies show pilots on specific and suitable examples, however, the truth is that scaling an AI is incredibly hard and it takes a significant amount of time to build an AI that is accurate on all possible user scenarios. We, at Attentive AI, try to mitigate this challenge by educating our customers transparently about the training period and the processes that are necessary to make the AI scalable. Thus our clients understand that the AI will not be scalable from the first day, rather through an iterative and active learning mechanism, which will help it to grow, and be more efficient,” said Dhawan.

In the rising age of AI, the key to success is to build artificial intelligence systems for a specific niche datasource to harness the power of data network effects, which Attentive AI is acing with their high-resolution geospatial imagery. “This gives us a competitive advantage while making cutting edge breakthroughs every week since we have worked on multiple use cases over a period from which our AI systems are continuously learning,” said Dhawan.

Future Prospective

Attentive AI, being one of the AI service provider, aims to create an accurate, constantly updating digital twin of the physical world. “We have only touched the tip of the iceberg as we are creating more AI technologies to analyse aerial imagery in specific geographies,” said Dhawan.

“We aim to build a global repository of multiple geospatial imagery sources and a suite of intelligent analytics for customers to request analytics on drones, streets, light detection and ranging (LIDAR), and all other kinds of geo-data sources at any time from anywhere,” concludes Dhawan.

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AI For Everyone: Super-Smart Systems That Reward Data Creators

Right now the answer tends to be: Large corporations. Data about our thoughts, preferences, fears and desires, as revealed in our emails, messages, …

Suppose a healthtech-oriented AI agent needs to make a hypothesis about which ones of the 25,000 or so human genes are involved in causing prostate cancer. But suppose it only has DNA data from a few hundred people – not enough to allow it to draw solid conclusions about so many different genes. Without a framework allowing this AI agent to consult other AI agents for help, the AI would probably just give up. But in a context like SingularityNET, where AIs can consult other AIs for assistance, there may be subtle routes to success. If there are other datasets regarding disorders similar to prostate cancer in model organisms such as mice, we may see progress on understanding which genes are involved in prostate cancer, via the combination of multiple AI agents, with different capabilities cooperating together.

Suppose AI #1 – let’s call it the Analogy Master – has a talent for analogy reasoning. This is the sort of reasoning that maps knowledge about one situation into a different sort of situation – for instance, using knowledge about warfare to derive conclusions about business. The Analogy Master might be able to use genetic data about mice with conditions similar to prostate cancer to draw indirect conclusions about human prostate cancer.

We will see work toward more general forms of AI that are owned and guided by individuals

Then, suppose AI #2 – let’s call it the Data Connector – is good at finding biological and medical datasets relevant to a certain problem, and preparing these datasets for AI analysis. And then suppose AI #3 – let’s call it the Disease Analyst – is expert at using machine learning for understanding the root causes of human diseases.

The Disease Analyst, when it’s tasked with the problem of finding human genes related to prostate cancer, may then decide it needs some lateral thinking to help it make a conceptual leap and solve the problem. It asks the Analogy Master, or many different AIs, for help.

The Analogy Master may not know anything about cancer biology, though it’s good at making conceptual leaps using reasoning by analogy. So, to help the Disease Analyst with its problem, it may need to fill its knowledge base with some relevant data, for example about cancer in mice. The Data Connector then comes to the rescue, feeding the Analogy Master with the data about mouse cancer it needs to drive its creative brainstorming, supporting the Disease Analyst to solve its problem.

All this cooperation between AI agents can happen behind the scenes from a user perspective. The research lab asking the Disease Analyst for help with genetic analysis of prostate cancer never needs to know that the Disease Analyst did its job by asking the Analogy Master and Data Connector for help. Furthermore, the Analogy Master and Data Connector don’t necessarily need to see the Disease Analyst’s proprietary data, because using multiparty computation or homomorphic encryption, AI analytics can take place on an encrypted version of a dataset without violating data privacy (in this case, patient privacy).

With advances in AI technology and cloud-based IT, this sort of cooperation between multiple AIs is just now becoming feasible. And, of course, such cooperation can happen in a manner controlled by large corporations behind firewalls. But what’s more interesting is how naturally this paradigm for achieving increasingly powerful and general AI could align with decentralized modalities of control.

What if the three AI agents in this example scenario are owned by different parties? What if the data about human prostate cancer utilized by the Disease Analyst is owned and controlled by the individuals with prostate cancer, from whom the data has been collected? This is not the way the medical establishment works right now. But at least we can say, on a technological level, there is no reason that AI-driven medical discovery needs to be monolithic and centralized. A decentralized approach, in which intelligence is achieved via multiple agents with multiple owners acting on securely encrypted data, is technologically feasible now, by combining modern AI with blockchain infrastructure.

Centralization of AI data analytics and decision-making, in medicine as in other areas, is prevalent at this point due to political and industry structure reasons and inertia, rather than because it’s the only way to make the tech work.

In this case, the original healthtech-oriented AI tasked with understanding the genetic causes of cancer would do well to connect behind-the-scenes with this analogy-reasoning AI, and with a provider of relevant model organism data to feed to the analogy reasoner, to get its help in solving its task.

In the Artificial General Intelligence network of the near future, the intelligence will exist on two different levels – the individual AI agents, and the coherent and coordinated activity of the network of AI agents (the combination of three AI agents in the above example; and combinations of larger numbers of more diverse AI agents in more complex cases). The ability to generalize and abstract also will exist, to some degree, on both of these levels. It will exist in individual AI agents like the Analogy Master in the example above, which are oriented toward general intelligence rather than toward solving highly specialized problems. And it will exist in the overall network, including a combination of generalization-oriented AI agents like the Analogy Master and special purpose AI agents like the Disease Analyst and “connector” AI agents like the Data Connector above.

The scalable rollout and broad adoption of decentralized AI networks is still near the beginning, and there are many subtleties to be encountered and solved in the coming years. After all, what the decentralized AI community needs to achieve its medium-term goals is more fundamentally complex than the IT systems that Google, Facebook, Amazon, IBM, Tencent or Baidu have created. These systems are the result of decades of engineering work by tens of thousands of brilliant engineers.

The decentralized AI community is not going to hire more engineers than these companies have. But then, Linux Foundation never hired as many engineers as Microsoft or Apple, and it now has the #1 operating system underlying both the server-side internet and the mobile and IoT ecosystems. If the blockchain-AI world’s attempt to catalyze the emergence of general intelligence via the cooperative activity of numerous AI agents with varying levels of abstraction is to succeed, it will have to be via community activity. This community activity will need to be self-organized to a large degree. But the tokenomic models underlying many decentralized AI projects are precisely configured to encourage this self-organization, via providing token incentives to AI agents that serve to stimulate and guide the intelligence of the overall network as well as working toward their individual goals.

Large centralized corporations bring tremendous resources to the table. However, for many applications – including medicine and advertising – it is not corporations, but individuals, who bring the data to the table. And AIs need data to learn. As blockchain-based AI applications emerge, large corporations may find their unique power being pulled out from under them.

Would you rather own a piece of medical therapies discovered using your medical records and genomic data? Would you rather know exactly how the content of your messages and your web-surfing patterns are being used to decide what products to recommend to you? Me too.

2020 will be the year that this vision starts to get some traction behind it. We will see the start of real user adoption for platforms that bring blockchain and AI together. We will see work toward more general forms of AI that are owned and guided by the individuals feeding the AI with the data they need to learn and grow.

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Top Movies Of 2019 That Depicted Artificial Intelligence (AI)

Artificial intelligence (AI) is creating a great impact on the world by enabling computers to learn on their own. While in the real world AI is still focused …

Artificial intelligence (AI) is creating a great impact on the world by enabling computers to learn on their own. While in the real world AI is still focused on solving narrow problems, we see a whole different face of AI in the fictional world of science fiction movies — which predominantly depict the rise of artificial general intelligence as a threat for human civilization. As a continuation of the trend, here we take a look at how artificial intelligence was depicted in 2019 movies.

A warning in advance — the following listicle is filled with SPOILERS.

Terminator: Dark Fate

Terminator: Dark Fate — the sixth film of the Terminator movie franchise, featured a super-intelligent Terminator named Gabriel designated as “Rev-9”, and was sent from the future to kill a young woman (Dani) who is set to become an important figure in the Human Resistance against Skynet. To fight the “Rev-9” Terminator, the Human Resistance from the future also sends Grace, a robot soldier, back in time, to defend Dani. Grace is joined by Sarah Connor, and the now-obsolete ageing model of T-800 Terminator — the original killer robot in the first movie (1984).

Spider-Man: Far From Home

We all know Tony Stark as the man of advanced technology and when it comes to artificial intelligence, Stark has nothing short of state-of-the-art technology in Marvel’s cinematic universe. One such artificial intelligence was the Even Dead, I’m The Hero (E.D.I.T.H.) which we witnessed in the 2019 movie — Spider-Man: Far From Home. EDITH is an augmented reality security defence and artificial tactical intelligence system created by Tony Stark and was given to Peter Parker following Stark’s death. It is encompassed in a pair of sunglasses and gives its users access to Stark Industries’ global satellite network along with an array of missiles and drones.

I Am Mother

I Am Mother is a post-apocalyptic movie which was released in 2019. The film’s plot is focused on a mother-daughter relationship where the ‘mother’ is a robot designed to repopulate Earth. The robot mother takes care of her human child known as ‘daughter’ who was born with artificial gestation. The duo stays in a secure bunker alone until another human woman arrives there. The daughter now faces a predicament of whom to trust- her robot mother or a fellow human who is asking the daughter to come with her.


Wandering Earth

Wandering Earth is another 2019 Chinese post-apocalyptic film with a plot involving Earth’s imminent crash into another planet and a group of family members and soldiers’ efforts to save it. The film’s artificial intelligence character is OSS, a computer system which was programmed to warn people in the earth space station. A significant subplot of the film is focused on protagonist Liu Peiqiang’s struggle with MOSS which forced the space station to go into low energy mode during the crash as per its programming from the United Earth Government. In the end, Liu Peiqiang resists and ultimately sets MOSS on fire to help save the Earth.

Alita: Battle Angel

James Cameron’s futuristic action epic for 2019 — Alita: Battle Angel is a sci-fi action film which depicts the human civilization in an extremely advanced stage of transhumanism. The movie describes the dystopian future where robots and autonomous systems are extremely powerful. To elaborate, in one of the initial scenes of the movie, Ido attaches a cyborg body to a human brain he found (from another cyborg) and names her “Alita” after his deceased daughter, which is an epitome of advancements in AI and robotics.


Jexi is the only Hollywood rom-com movie depicting artificial intelligence in 2019. The movie features an AI-based operating system called Jexi with recognizable human behaviour and reminds the audience of the previously acclaimed film ‘Her’, which was released in 2014. But unlike Her, the movie goes the other way around depicting how the AI system becomes emotionally attached to its socially-awkward owner, Phil. The biggest shock of the comedy film is when Jexi — the AI which lives inside Phil’s cellphone acts to control his life and even chases him angrily using a self-driving car.

Hi, AI

Hi, AI is a German documentary which was released in early 2019. The documentary was based on Chuck’s relationship with Harmony — an advanced humanoid robot. The film’s depiction of artificial intelligence is in sharp contrast with other fictional movies on AI. The documentary also depicts that even though human research is moving in the direction of creating advanced robots, interactions with robots still don’t have the same depth as human conversations. The film won the Max Ophüls Prize for best documentary for the year.

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Artificial Intelligence Service Market Size & Forecast 2019-2025|International Business Machines …

The report titled “Global Artificial Intelligence Service Market Size, Status and Forecast 2019-2025” provide (6 Year Forecast 2019-2025) enhanced on …

Artificial Intelligence Service Market

The report titled “Global Artificial Intelligence Service Market Size, Status and Forecast 2019-2025” provide (6 Year Forecast 2019-2025) enhanced on worldwide competition by topmost prime companies (International Business Machines, SAP, Google, Amazon, Salesforce, Intel, Baidu, Fair Isaac Corporation(FICO), SAS Institute(US)) which providing information such as Company Profiles, Capacity, Gross, Gross Margin, Product Picture and Specification, Production, Price, Cost, Revenue and contact information. This Artificial Intelligence Service market report crucial insights that facilitate the Market Trends, Drivers, Market Dynamics, Overview, Scope, Definitions, Classifications, Opportunities, Competitive Landscape. The report provides key statistics on the market status of the Artificial Intelligence Service market manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.

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Scope of Artificial Intelligence Service Market: Verticals in the AI as a service market include Banking, Financial Services, and Insurance (BFSI), healthcare and life sciences, retail, telecommunications, government and defense, manufacturing, energy, and others (Education, Agriculture, Transportation, and Media and Entertainment). AI as a service helps various verticals easily integrate AI capabilities with their business applications. Moreover, by leveraging the benefits of AI as a service, verticals can focus more on the enhancement of their business processes and formulation of growth strategies, rather than worrying about costs related to the purchase and maintenance of AI-powered platforms and tools.

The growing demand for AI-powered services in the form of Application Programming Interface (API) and Software Development Kit (SDK) and increasing number of innovative startups are some of the major factors that are driving the growth of the AI as a service market. The demand for intelligent business processes is growing across verticals to enhance the overall experience that is delivered to their customers. Organizations are therefore focusing on integrating AI capabilities with their existing business applications. However, the process of building APIs is time- consuming. Hence, various verticals are focused toward the adoption of easily available AI-powered APIs and machine learning models to solve their business problems.

On the basis of product, this report displays the sales volume, revenue (Million USD), product price, market share and growth rate of each type, primarily split into-

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Artificial Intelligence Service Market: Regional Analysis Includes:

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How Much Healthcare AI Market is Growth in Next 5 years | AiCure, APIXIO, Atomwise, Butterfly …

Top Key Players are including in this report: AiCure, APIXIO, Atomwise, Butterfly Network, Cyrcadia Health Inc., Enlitic Inc., IBM, iCarbonX, Insilico …

A complete analysis of Healthcare AI market is been done in this intelligence report. It includes the investigations done of the past progress, present ongoing market scenarios and future prospects. In this particular market report an accurate data of the products, strategies and market shares of leading companies are mentioned clearly.

It clarifies a thorough synopsis of Healthcare AI market dependent on the central parameters. End users, merchandises, provinces and many other subdivisions are planned and elucidated. A transitory idea about the dynamic forces which help make the market more prosperous are deliberated in order to help customer appreciate the future market situation.

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Top Key Players are including in this report: AiCure, APIXIO, Atomwise, Butterfly Network, Cyrcadia Health Inc., Enlitic Inc., IBM, iCarbonX, Insilico Medicine Inc., Lifegraph, Modernizing Medicine, Pathway Genomics Corporation etc.

It provides an in-depth analysis of different attributes of industries such as trends, policies, and clients operating in several regions. The qualitative and quantitative analysis techniques have been used by analysts to provide accurate and applicable data to the readers, business owners and industry experts.

The analysts have distributed the global Healthcare AI market into several regions such as North America, Latin America, Asia-Pacific, Africa, and India for detailed study. The financial aspects of the business have been scrutinized by considering the several attributes such as price, profit margins and sales by regions in the forecast period.

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Key Questions Answered in this Report:

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Leading key players have been profiled to get a clear insight into the businesses on the basis of successful strategies of the top level companies. Along with this, it also offers competitive significance among the several traders by presenting the facts and figures of market shares.

This research highlighting the current scenario of the global Healthcare AI market and focuses on some significant issues faced by various stakeholders.

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