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      ["title"]=>
      string(45) "10 Big Ideas that will Shape PropTech in 2020"
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    2019 was an incredible year for PropTech.

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    From the highest highs (a record $16 billion in VC investments) to the lowest lows (WeWork implosion), 2019 may well go down as one of the most eventful years for PropTech to date. The cautious excitement coupled with the possibility of explosive growth ahead is palpable. Will 2020 be the year that finally delivers this growth? I am no clairvoyant, but I can’t help to think that 2020 will be big. Likely bigger than 2019. Here are my predictions for the 10 big ideas that will shape PropTech in 2020: 

    1. Early Adopters of PropTech will Start to Break Away from the Competition

    As a PropTech provider, I am obviously biased in making this statement and projecting the impact of tech on the real estate industry. For objectivity though, one only has to look at adjacent industries (Hospitality Travel, Retail, etc.) to see how players who embraced tech fared compared to their peers who did not. Take retail for example. While some players vanished (Circuit City) and others became a shadow of their former self (Sears), there are some that emerged bigger and stronger (Best Buy). Why? They embraced tech wholeheartedly to not just break away from their competition but also avoid being disrupted by tech giants (e.g., Amazon).

    Fortunately, other industries took notice and adopting digital tech has become a $2.3 trillion dollar business worldwide. The real estate industry will be no different. I expect 2020 to be the year where players who wholeheartedly embrace tech - specifically PropTech - will start to break away from their competition, leapfrog them (2022-23) and eventually obliterate them (beyond 2025). 

    2. PropTech will Need to Provide a Clear ROI

    While PropTech’s early success came from the focus on the user of space (e.g., resident, tenant, etc.), its growth will increasingly depend on its ability to deliver a financial return to the managers of space (e.g., operators, property managers). How does PropTech increase revenue? Reduce operational costs? Increase operational efficiency? Increase renewals? These are the kind of questions PropTech will increasingly face and will have to answer in a ROI positive way in 2020.

    For example, it’s great the PropTech increases digital engagement of residents. But does this engagement actually cause residents to stay longer in their leases? If so, how much additional revenue and cost impact does it bring? And is the impact a multiple of the PropTech costs, thereby ROI? Expect more such conversations and expectations from PropTech in 2020 

    3. We will Go from ‘Collecting’ Data to ‘Acting’ on it

    Such is the onslaught of data in the real estate space that it’s hard to go into an industry conference and not have data as a key discussion topic. From data about how properties are performing to how residents are interacting with physical space to the millions of data points being generated by smart devices (smart locks, digital home assistants); it is no secret that we are in the midst of a data tsunami. 

    But collecting data for data sake is futile. The real power of data lies more in the insights that can be derived from it and the resulting actions that such insights help trigger. And I expect 2020 to be the year where PropTech starts doing exactly that.

    For example, imagine being able to act on resident engagement data to reduce marketing spend by targeting only those residents who are likely to churn vs all whose lease may be up for renewal. Or in the case of operations, imagine being able to act on property performance data to increase training efficacy by training only those staff who have higher than average response times to work orders. Or when it comes to investment decisions, imagine being able to make better ones by getting real answers to thorny questions such as: why two similar properties in similar zip codes exhibit stark differences in their ability to command rent? And is the difference really attributed to factors you control (e.g., type of property, amenity, etc.) or those you don’t (e.g., density of 5-star rated restaurants in close proximity)?

    Expect data to be your friend for making such decisions in 2020 and acting on them 

    4. Experience-First will Become the Default

    While PropTech has and will always be about the physical space, its success will ultimately hinge on solving pain-points for those who interact with this space. Whether these are people who occupy these spaces (residents, tenants, etc.) or manage them (operators, property managers, etc.), it is critical to remember that in their day-to-day life, they are regular consumers like you and me. And when it comes to consumers like you and me, it is safe to say that technology has spoiled us so much in our day-to-day that arguably the only thing more important than ‘what we want,’ is ‘how we want it.’ (e.g., the only thing more important than getting a ride is to get it via a one-click)

    This desire for experiences that are on-demand (you can hail a Lyft anytime), ubiquitous (anywhere), convenient (one-click), reliable and affordable will require PropTech to take an experience-first approach to shape their value proposition in 2020.

    Consider this: an experience-first approach may incent residents (think reward points) to pick up their packages instead of just sending them a standard twice-a-week reminder that they have a package waiting. Whether it’s adopting that latest consumer trends to provide 24x7x365 digital connectivity, smart home technology to enable seamless access, or a one-click FinTech to deliver digital payments, expect more customer experience-centric innovations from PropTech in 2020.  

    5. VC Investments in PropTech will Rise Further

    It is no secret that real estate and property management companies have been adopting PropTech at a feverish pace over the last few years. So much so that a closer look at the last few years of VC investments reveals a steady rise:

    2017 - $12.6B

    2018 - $9.6B

    2019 - $16B (through mid-year 2019)

    What’ll it be in 2020? A specific number is anyone’s guess but going by the trends, I expect this number to continue to rise. Why? Plateauing property prices, a desire to provide greater service levels and drive operational efficiencies will continue to drive higher VC investments in 2020.

    6. App proliferation will Increase. However, App Fatigue may start to kick-in 

    One of the implications of higher VC investment is that more and newer players will continue to enter the PropTech space. And because apps are a popular method for PropTech players to deliver their services, an influx of new players will also result in further proliferation of apps. 

    One downside to this - and something we have seen in the consumer tech space too - could be app fatigue, where consumers (residents) do not want to download any more apps. I don’t think we are there for PropTech yet. However, towards the latter part of 2020, I would not be surprised if app fatigue starts to kick in. It will be important for apartment operators to consider app consolidation or how to work with PropTech partners to streamline the number of apps needed. 

    7. Digital & Automation will go beyond Self-Guided Tours and Chatbots

    Few topics seem to get so much interest in PropTech as does automation. Freeing humans from repetitive, low-value tasks has long been the promise of automation. The only difference now for PropTech is that promise is finally coming true. Glimpses of automation are already here as evident with self-guided tours, chatbots and digital work orders. However, I believe the day where the leasing office has a digital assistant isn’t too far away and could gain traction in 2020. This assistant will not only review a list of all possible tasks needing action but - more importantly - will have the intelligence to direct staff time and attention to only the one(s) needing urgent attention, such as making a personalized outreach to a resident regarding a 3-day-old unresolved maintenance request. 

    8. The Topic of Security will go beyond IT Department into Corporate Boardroom

    An implication of more VC funding and app proliferation will be that as real estate companies adopt more PropTech, they will increasingly rely on their PropTech providers for solutions, including those related to security. And because many PropTech providers are digital natives with applications in the cloud instead of data centers, potential for exposure to cybersecurity risks will likely make security go beyond the IT departments. Specifically, topics that were previously relegated to IT departments - like encryption, identify/access management and better incident tracking and response - will increasingly be discussed in the corporate boardrooms. The good news is that PropTech providers are digital natives and I expect them to flaunt their cybersecurity capabilities as a key competitive advantage. 

    9. Savvy Players will Adopt Innovative Approaches to Bridge the Digital Talent Gap

    It is no secret that attracting the right digital talent is challenging in current times because of alternative options – e.g., Facebook, Amazon, Netflix, Google etc. - and job prospects such talent has. But savvy real-estate players, will not let this delay their plans to embrace tech. Instead, they will take charge of their destiny and start adopting talent innovation. Specifically, I expect they them to start identifying internal talent who can be re-trained to meet the demands of a digital economy and workforce. Expect more “up” and “re” skilling in fields such as IT, digital marketing, automation and more. 

    10. Change will not be Easy but Necessary

    While the above ideas and innovations are promising, they also represent a change from the status quo. And change is never easy. Especially for an asset class like real estate that has not needed to change in a long time.

    But I also feel that this change will be necessary. 

    There’s ample evidence from other industries on the need for this change. We already saw that for every Best Buy that changed, there was a Circuit City that did not. For every Netflix that changed, there was a Blockbuster that did not. And for every iPhone camera that sold, there was a Kodak that did not. And it’s not because they did not try or want to change. They did. But that is the thing about change. It is never easy. Where some succeed, others fail. 

    In 2020, for every real estate company that will grab the opportunity that change presents, there will be one that will not.

    These are all I have for now. It's virtually impossible to get these right. But getting them right is not my goal for writing these. But I do hope this made you think. As much as writing these made me think. Do you agree with my predictions? Why or why not? Please leave your thoughts in the comments below. Towards the end of 2020, I’ll revisit my predictions and assess how accurate or off they were.

    Here’s to an exciting 2020.

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    10 Big Ideas that will Shape PropTech in 2020

    Nitin Vig
  • 2
    object(stdClass)#13887 (59) {
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      ["title"]=>
      string(66) "The Role of Behavior Trees in Robotics and Artificial Intelligence"
      ["alias"]=>
      string(66) "the-role-of-behavior-trees-in-robotics-and-artificial-intelligence"
      ["introtext"]=>
      string(193) "

    Behavior trees can help design robots that are highly adaptive and reactive to their environment.

    " ["fulltext"]=> string(5053) "

    A behavior tree is a way to design the switching between multiple tasks in autonomous devices such as a robot. It consists of behaviors or nodes connected to each other in the form of a tree, starting with a single root node. Behavior trees can help in creating an efficient method of designing complex algorithms and systems that are both modular and reactive. Modularity means the degree to which the components of a system can be separated into individual blocks and then recombined. Reactivity implies the ability of an algorithm to react to changes efficiently. Behavioral trees can be designed, tested, and implemented one module at a time. They can also be highly responsive to the environment around them. It is because of these features that behavior trees will play an important role in robotics and AI development and have an advantage over finite-state machines. A finite state machine consists of a set of predefined states and a set of transitions between these pairs of states. Thus, they tend to be unusable in complex systems that depend on the environment to carry out actions. FSMs are highly susceptible to human errors, hard to modify with increasing transitions, and are impractical to reuse. Thus, FSMs become limited in designing a complex robotics system, and this is where behavior trees step in.

    The Role of Behavior Trees in Robotics and Artificial Intelligence

    Imagine a robot employed in a warehouse tasked with the duty to place objects from the assembly line onto particular shelves. An FSM would take the object and then start walking towards the shelves. It won’t pause or stop if an employee comes in its path or if the object falls down from the robot’s grip. The robot continues with the task even though the end result will remain incomplete. This results not only in the loss of productivity for that instance but also can cause a butterfly effect and result in more inefficiencies.

    The same robot designed with behavior trees is highly modular and reactive. The actions of the robot are carried out or aborted according to ticks, which depend on the leaf node’s return statuses. In simpler words, if the robot faces an obstacle in its path, it stops with the assigned task and returns to a safe position. A safe position, in this case, can be waiting for the human to clear the path or returning back to the assembly line and alerting the employee about the incident. Thus, behavioral trees can be used to design a highly responsive robotics system. Therefore, robots programmed with behavior trees prove highly beneficial as cobots as they can react and respond effectively to their environment.

    The Role of Behavior Trees in Robotics and AI

    Behavior trees have proven beneficial in training AI and robotics. Industry leaders attribute modularity as the main reason for the prominence of behavior trees in AI and robotics. Modularity is a key enabler for designing complex and reusable systems that are currently being used in AI and robots. Thus, behavior trees have gained the confidence of industry leaders in robotics and AI. Mike Weldon, a programmer at Disney, Pixar in his book, Behavior Trees in Robotics and AI: An Introduction wrote, “There are a lot of different ways to create AI’s, and I feel like I’ve tried pretty much all of them at one point or another, but ever since I started using behavior trees, I wouldn’t want to do it any other way. I wish I could go back in time with this information and do some things differently.”

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    The Role of Behavior Trees in Robotics and Artificial Intelligence

    Naveen Joshi
  • 3
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      string(177) "

    The boundaries of cybersecurity will be manipulated by the advances in artificial intelligence, the evolution of digital threats, and on ever-adapting leadership. 

    " ["fulltext"]=> string(2216) "

    Podcast

    I had a great time being interviewed by Vaishali Lambe [Lisha] in her podcast SoLeadSaturday because we talked about how cybersecurity and AI are intertwined, how leadership is crucial, and the fact that technology tools are being used for both good and malicious purposes. The growing demands for a security-savvy workforce led us to explore the vast opportunities in the field. Emerging factors magnify the interesting swirls of competing challenges. To cap the discussion, we visualized the future of the industry and discussed the risks.  

    I provided insights to those interested in joining the cybersecurity professional community. Success requires a more inclusive and diverse workforce which includes a higher emphasis on the participation of women and underrepresented minorities. 

    The Entanglement of AI and Cybersecurity Podcast

    Overall a great discussion. I had a lot of fun and look forward to future talks with Lisha!

    AI and cybersecurity are growing together and the future is still largely unknown. Where do you think the risks and opportunities will emerge? Let me know what you think in the comments below or follow me on LinkedIn or Medium.

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    The Entanglement of Artificial Intelligence and Cybersecurity

    Matthew Rosenquist 
  • 4
    object(stdClass)#13957 (59) {
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      ["title"]=>
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      string(222) "

    With the help of gradient descent algorithms, coders can reduce the cost function and increase the optimization of algorithms.

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    ML algorithms and deep learning neural networks work on a set of parameters like weights and biases, and a cost function that evaluates how good a set of parameters is. The lower the cost function goes, the greater is the accuracy of training data and output. To reduce this cost function is one of the key aspects of optimization in AI models. There are various optimization algorithms available that can be used to optimize AI models. And, the gradient descent algorithm is a highly popular optimization algorithm from them.

    How a Gradient Descent Algorithm Works

    Suppose you have a ball and you place it on an inclined plane, according to the gravitational law, the ball will roll downwards until it rests on a gentle plane and stops rolling. Gradient descent algorithm functions in a similar way to the above example. Suppose the ball in the above example is the cost function, then gradient descent algorithms will act as a gravitational force to bring it down until it reaches an optimal value which would be the gentle plane. Every time the gradient descent algorithm runs, it calculates a cost function and then it iterates to find whether the cost function can be further minimized or not. And, this process continues till it finds the optimal cost function which cannot be minimized. The gradient descent starts with a set of parameters, and then it improves them slowly. The gradient or derivative calculated tells the developer the incline or slope of the cost function. Then to reduce the cost, the developer moves in the direction opposite to gradient. The gradient is calculated with the help of different types of gradient descent algorithms.

    What are the Different Types of Gradient Descent Algorithms?

    On the basis of the amount of data a gradient descent algorithm ingests, it can be classified into three types:

    Optimizing Artificial Intelligence Algorithms with Gradient Descent

    Batch Gradient Descent

    It computes the gradient of the cost function with respect to the parameter for entire training data. Since batch gradient descent calculates the gradient for the entire dataset to perform one parameter update, it can be very slow.

    Stochastic Gradient Descent

    It computes the gradient for each data using a single training point chosen at random. Therefore learning happens after computing the gradient for each data. Since the gradient is calculated individually for each data, the initial calculation becomes very slow.

    Mini-Batch Gradient Descent

    In mini-batch gradient descent, first, the model developer divides the entire dataset into mini-batches, and then the gradient is calculated for each mini-batch. It becomes faster than batch gradient descent as while a parameter update, it has to go through a lot less data. But while minimizing the cost function, the gradient curve in mini-batch gradient descent goes back and forth and does not converge. Hence, it becomes difficult to get the exact learning curve.

    Gradient descent algorithms work mostly fine, but there are some challenges because of which gradient descent might not function properly. For instance, if the input data is arranged in a way that it poses a non-convex optimization problem like invex functions, random coordinate descent, and issues with convergence rate, then it becomes difficult to perform optimization with the help of gradient descent algorithms. The difficulty arises as for non-convex optimization, the incline and slope of gradient become difficult to find.

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    Optimizing Artificial Intelligence Algorithms with Gradient Descent

    Naveen Joshi
  • 5
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      ["title"]=>
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      string(227) "

    The oil and gas industry has benefited heavily in major areas from artificial intelligence (AI), and seismic interpretation is the next area to experience the revolutionary impact.

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    By building an effective conceptual model of geological areas, AI can help in streamlining the process of oil and gas exploration.

    Seismic interpretation is the science (and art) of inferring the geology at some depth from the processed seismic record. Seismic interpretation is necessary for the oil and gas industry to discover and develop profitable fields and reserves. Historically, oil companies have had to rely on a trial and error approach. However, with seismic interpretation, the process was streamlined significantly but yet wasn’t highly efficient. The process, although improved the accuracy of finding an oil reserve, was still highly time-consuming and resource-intensive.

    Challenges in Seismic Interpretation and Resource Exploration

    When it comes to geological exploration, major faults are easier to identify, but identifying smaller cracks is a tedious task. The subtle faults can be overlooked by human scientists and can take away the opportunity of drilling a resource-rich reserve. Also, the process is highly time-consuming, requiring scientists to analyze tons of data. AI can help overcome these challenges as it automates the process and results in a quick method of oil exploration.

    The Transformative Partnership of AI and Seismic Interpretation

    Deep learning systems can help simplify the process of seismic interpretation. AI algorithms can easily and quickly identify fault systems from 3D seismic images data provided to them. The algorithm can help spot subtle faults that might be overlooked by human scientists. The deep learning model is trained with geological data. The AI model then analyses the data and helps predict faults that may be present in the region. Oil companies can then plan their drilling and rigging activities based on the output provided by the AI algorithm. They can assist in accurately determining the availability of natural gas and oil in areas that are inaccessible or are hard to access for humans. The organization can then determine whether to drill in the fracture or avoid it, depending upon the quantity of oil and petroleum reserves in the fault. For instance, researchers at the University of Texas in Austin have used synthetic data to train neural networks to spot small faults that might have been missed by humans. 

    AI and seismic interpretation 

    AI has proved highly beneficial for the oil and gas sector. Although it can significantly improve the process of mapping geological regions, the technology surely isn’t foolproof. A perfect balance for using AI for seismic interpretation would be the combination of AI and human interpreters. Human reasoning is no match for technology, and humans can’t work with the speed of technologies like AI and seismic interpretation can thus be improved significantly only with their combination. This combination of man and machine can help improve speed, quality, and understanding of geological mapping significantly, and seems like the logical solution for improving seismic interpretation.

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    How Artificial Intelligence is Helping Seismic Interpreters

    Naveen Joshi

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