The Sharer: New Blog on Search and Knowledge

A new blog by colleague Sundar Kadayam of Zakta. that I am following: " ... I am a technology entrepreneur. I have more than 23 years of experience in the software industry. I have been fortunate to be involved with many successful, award-winning products and industry-firsts. In 2007, I founded Zakta, where I work as its CEO. Zakta’s mission is to deliver Web search for YOU and people YOU trust. Zakta makes searching more purposeful by helping you find information faster, keep what you find and collaborate with others. SearchTeam.com, by Zakta, is the world’s first real-time collaborative search engine ... " .  

See also some of his novel collaborative search work at Zakta.com.

Blekko: A Slashtag Search Engine

Every now and then there is an attempt at a new take on the search engine.  Zakta is an example I took a look at that attempts to build specialized research experiences.  I much liked its approach of looking at the problem from a collaborative search view.   Now released is dispassionately named Blekko  " ... The service offers an interesting way to “slash” or create specialty search engines for any topic, along with new features the company hopes will improve relevancy ... " .  An overall look at Blekko here.  You can try it in Beta here.

So what is a slashtag search?  " ... slashtags search only the sites you want and cut out the spam sites. use friends, experts, community or your own slashtags to slash in what you want and slash out what you don't .... " .   I can see that useful at times, but isn't it also overly restrictive?   So Blekko is inclusive/restrictive rather than collaborative.  Worth looking at.

Update: And some negative comments on Blekko, saying it is broken.  And another critical view.
-

Collaborative Search: SearchTeam

I wrote a note about Zakta's new Collaborative Search Tool: SearchTeam back in mid July.  Wanted to repeat that as I start to take a yet deeper dive look at the tool. I think this is really innovative idea.  Could have used this often when doing research and development.   Now thinking of several applications I am working on now.  Here is my previous post which had lots more links and details.  Try it!  The first fundamentally new search idea I have seen for some time.

SearchTeam Emerges

I have seen some fascinating early versions of SearchTeam, the real-time collaborative search engine from Zakta..  A great idea, think of it for collaborative use in a number of situations.   Say you are researching a new innovation, a technology detail, a competitive intelligence problem.  With an eye to later curating and understanding your results.  And of course you are always collaborating with users as you find, check and  document search results. We often saw that need in the enterprise.  It reminds me of the functionality in Vannevar Bush's proposed Memex system.  Nicely done.

Now it is here.   I have been testing it for some time in early forms, and it is very useful to do searches, share the results, and deliver value in ways it was cumbersome to do before.
See their concept video and a guided tour.


SearchTeam, in a nutshell:

SearchTeam enables trusted people to search the Web together, curating their search results, their thoughts and opinions, as well as relevant documents on a given topic, in one place (a SearchSpace).

Other Related Resources:

SearchTeam blog: http://blog.searchteam.com/
SearchTeam on Twitter: @SearchTeamNews / http://twitter.com/SearchTeamNews
SearchTeam on Facebook: http://www.facebook.com/pages/SearchTeam/174952665849873

Contact information:

Via Sundar Kadayam, Founder and CEO, Zakta
Email: skadayam@zaktallc.com          
Personal Blog: The Sharer - http://kadayam.com/
LinkedIn Profile: http://www.linkedin.com/in/sundarkadayam

Collaborative Asynchronous Searching

I recently mentioned SearchTeam.  I see it has now been looked at and reviewed in detail in Information Today,  Note in particular the description of the Semantic Topic network, a novel and powerful capability.

"  ... Zakta launched a new collaborative search site, SearchTeam.com, on July 12. Proposing to “search the web together with people you trust,” SearchTeam lets groups of searchers save results, add notes, modify results, “like” specific hits, and share results with others. Rather than requiring that the entire team (or group of collaborators) be online at the same time, SearchTeam is asynchronous, letting each team member save results, annotate them, and even upload files to share with others. As described in the SearchTeam FAQ, searchers can create a SearchSpace for gathering and organizing search results for each topic. “You can find and save only what you want while you are searching and throw away what you don't want or find irrelevant. You can automatically organize what you save, into folders of your choosing.” ... "

Boutique Search Again

Reminds me of the early days of search and things we set up for specialty use.   Especially healthcare based, but could be related to any domain.    Curated web and supporting search engines.    Recall setting up a search engine pointed (curated to) directly at a specific project.   See 'Zakta' as an example, is it still available? 

The Future of Search Is Boutique, By Sari Azout in future.a16z

This is an edited version of a post that originally ran here. 

For most queries, Google search is pretty underwhelming these days. Google is great at answering questions with an objective answer, like “# of billionaires in the world” or “What is the population of Iceland?” It’s pretty bad at answering questions that require judgment and context like “What do NFT collectors think about NFTs?”

The evidence is everywhere. These days, I find myself suppressing the garbage Internet by searching on Google for “Substack + future of learning” to find the best takes on education. We hack Twitter with the “what is the best” posts over and over again. When I’m researching a new product, I type “X item reddit” into Google. I find enormous value in small, niche, often forgotten sites like Spaghetti Directory.

There’s an emergence of tools like Notion, Airtable, and Readwise where people are aggregating content and resources, reviving the curated web. But at the moment these are mostly solo affairs — hidden in private or semi-private corners of the Internet, fragmented, poorly indexed, and unavailable for public use. We haven’t figured out how to make them multiplayer. In cases where we’ve made them public and collaborative — here is a great example — these projects are often short-lived and poorly maintained.

The stated mission of a company worth almost two trillion dollars is to “organize the world’s information” and yet the Internet remains poorly organized. Or, stated differently, in a world of infinite information, it’s no longer enough to organize the world’s information. It becomes important to organize the world’s trustworthy information.  .... ' 

Zakta is on the Technology List to Watch

I have long followed Zakta's approach for collaborative search and enabling social intelligence.  I see they have been declared one of the ten technology companies to watch in 2013.  Inside Analysis Writes: 

" ... Zakta: Semantic technology is particularly useful in the kind of detailed research activities where you need to trawl through all available information and assemble a virtual research resource that excludes the irrelevant and identifies what’s important. This can, in our opinion, only be achieved by semantic search, which is part of what Zakta provides. Google is, by comparison, a very blunt instrument. Zakta offers series of tools and capabilities: Zresearch, Zmagnify, Zlearn and Zguides. ... " 

DSC Surveys Data Science Techniques

Index to useful article lists from Data Science Central.  Click through to their site for search details.  Join the the group.  Very nicely done articles from introduction to in-depth.  I have not linked them all, but go to DSC for more.

" ... These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understand the technique in question, and come with further references and source code.

Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science. However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately used) than the deep data scienceequivalent. To learn more about deep data science,  click here. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence.

Finally, to discover in which contexts and applications the 40 techniques below are used, I invite you to read the following articles:

• 21 data science systems used by Amazon to operate its business
• 24 Uses of Statistical Modeling

   40 Data Science Techniques

1. Linear Regression 
2. Logistic Regression 
3. Jackknife Regression *
4. Density Estimation 
5. Confidence Interval 
6. Test of Hypotheses 
7. Pattern Recognition 
8. Clustering - (aka Unsupervised Learning)
9. Supervised Learning 
10. Time Series 
11. Decision Trees 
12. Random Numbers 
13. Monte-Carlo Simulation 
14. Bayesian Statistics 
15. Naive Bayes 
16. Principal Component Analysis - (PCA)
17. Ensembles 
18. Neural Networks 
19. Support Vector Machine - (SVM)
20. Nearest Neighbors - (k-NN)
21. Feature Selection - (aka Variable Reduction)
22. Indexation / Cataloguing *
23. (Geo-) Spatial Modeling 
24. Recommendation Engine *
25. Search Engine *
26. Attribution Modeling *
27. Collaborative Filtering *
28. Rule System 
29. Linkage Analysis 
30. Association Rules 
31. Scoring Engine 
32. Segmentation 
33. Predictive Modeling 
34. Graphs 
35. Deep Learning 
36. Game Theory 
37. Imputation 
38. Survival Analysis 
39. Arbitrage 
40. Lift Modeling     ..... " 

Follow us on Twitter: @DataScienceCtrl 

Scoping from Big Data to Big Knowledge

 This was brought to my attention by the 'Window Weekly Podcast' this week, in part because it dealt with conversations we had with Linkedin before they were acquired by Microsoft.   This did not lead anywhere, but touched on many aspects of how to handle corporate knowledge effectively.  See also the similarity to another system, called Zakta, which we called  a 'Collaborative Search Engine'.  See Zakta.com   Which uses classifications of kinds of knowledge.   Which we tested early on.  Worth a look.  

Project Alexandria is a research project within Microsoft Research Cambridge dedicated to discovering entities, or topics of information, and their associated properties from unstructured documents. This research lab has studied knowledge mining research for over a decade, using the probabilistic programming framework Infer.NET. Project Alexandria was established seven years ago to build on Infer.NET and retrieve facts, schemas, and entities from unstructured data sources while adhering to Microsoft’s robust privacy standards. The goal of the project is to construct a full knowledge base from a set of documents, entirely automatically.

The Alexandria research team is uniquely positioned to make direct contributions to new Microsoft products. Alexandria technology plays a central role in the recently announced Microsoft Viva Topics, an AI product that automatically organizes large amounts of content and expertise, making it easier for people to find information and act on it. Specifically, the Alexandria team is responsible for identifying topics and rich metadata, and combining other innovative Microsoft knowledge mining technologies to enhance the end user experience. ... '

https://www.microsoft.com/en-us/research/blog/alexandria-in-microsoft-viva-topics-from-big-data-to-big-knowledge/   Originally in MS Research.   ... ' 

The Army Studies Real Time Conversation

Considerable, interesting piece on the topic. Leads to the open question about how we converse with our robot assistants.   And leads on to other kinds of hybrid, cooperative work.   And AI to provide useful information about the meaning of statements and commands in context.    Expect lots more in this space in the coming years.

Army researchers create pioneering approach to real-time conversational AI  by The Army Research Laboratory  in Techexplore.

Spoken dialogue is the most natural way for people to interact with complex autonomous agents such as robots. Future Army operational environments will require technology that allows artificial intelligent agents to understand and carry out commands and interact with them as teammates.

Researchers from the U.S. Army Combat Capabilities Development Command, known as DEVCOM, Army Research Laboratory and the University of Southern California's Institute for Creative Technologies, a Department of Defense-sponsored University Affiliated Research Center, created an approach to flexibly interpret and respond to Soldier intent derived from spoken dialogue with autonomous systems.

This technology is currently the primary component for dialogue processing for the lab's Joint Understanding and Dialogue Interface, or JUDI, system, a prototype that enables bi-directional conversational interactions between Soldiers and autonomous systems.

"We employed a statistical classification technique for enabling conversational AI using state-of-the-art natural language understanding and dialogue management technologies," said Army researcher Dr. Felix Gervits. "The statistical language classifier enables autonomous systems to interpret the intent of a Soldier by recognizing the purpose of the communication and performing actions to realize the underlying intent."

For example, he said, if a robot receives a command to "turn 45 degrees and send a picture," it could interpret the instruction and carry out the task.

To achieve this, the researchers trained their classifier on a labeled data set of human-robot dialogue generated during a collaborative search-and-rescue task. The classifier learned a mapping of verbal commands to responses and actions, allowing it to apply this knowledge to new commands and respond appropriately....  " 

Social Integration in Search

Social integration of search Is mentioned as being deepened by Bing and Google+.  We examined this general idea for some time as it related to corporate knowledge management.  There is a company that does exactly this: Zakta.com.  Which aims to link search, probably one of the most common elements of business process done today, to collaborative business focus.

The Science Fiction Behind Search

In the Google Quarterly. A looking at what works very well, not quite so well and not at all in search by Google, and how we may start to think about the future of search. I think one thing missing is strong collaborative support, like in the newly released SearchTeam.

Collaboration with Lists and Search

In CWorld:  Short view on the continued evolution of collaboration software.  Its acceptance within the enterprise.   As they say there,  this started with a very simple idea, the to-do list.    Most collaboration that exists is based on that humble idea.   Yet another way to look at collaboration is via a common task.  One that needs to be more collaborative and focused.  And what is done more often than search?  See Zakta's approach to this, which I have now reviewed a number of times.   Worth examining.

Tiny Robotics

Very tiny robotics,  collaborative?

A new micro aerial robot based on dielectric elastomer actuators

by Ingrid Fadelli , Tech Xplore

A 0.16 g microscale robot that is powered by a muscle-like soft actuator. Credit: Ren et al.

Micro-sized robots could have countless valuable applications, for instance, assisting humans during search-and-rescue missions, conducting precise surgical procedures, and agricultural interventions. Researchers at Massachusetts Institute of Technology (MIT) have recently created a tiny, flying robot based on a class of artificial muscles known as dielectric elastomer actuators (DEAs).

This new robot, presented in a paper published in Wiley's Advanced Materials journal, significantly outperformed many DEA-based micro-systems developed in the past. Most notably, the robot can operate at low voltages and has high endurance despite its miniature size.

"Our group has a long-term vision of creating a swarm of insect-like robots that can perform complex tasks such as assisted pollination and collective search-and-rescue," Kevin Chen, one of the researchers who carried out the study, told Tech Xplore. "Since three years ago, we have been working on developing aerial robots that are driven by muscle-like soft actuators." ... ' 

Amapedia

I just noticed this, the Amapedia is a wiki for owners of products being sold by Amazon. Wikipedia inspired, with an included classification system, called 'collaborative structured tagging' (see below):

"What is Amapedia?
Amapedia is a community of users who share information about the best products they own and love. By submitting information about your favorite products you will be joining a community where people freely share their knowledge and give their opinions and advice. You are able to read about the latest and best products in the market today from the viewpoint of real people who purchased the products you are interested in.

What should I put into an Amapedia article?
Think of an Amapedia article as a product encyclopedia entry that everyone who comes to an Amazon product page can see. Amapedia introduces a new model for jointly constructing a product encyclopedia called "collaborative structured tagging": in addition to writing free-form Wiki text you can also tag a product with what it "is" (e.g. a "Vacuum Cleaner") and what its facts are (e.g. "bagless" and "Manufacturer: Hoover"). Amapedia then uses these structured tags to let you discover, search, and compare related products in novel ways ... "

Can AlphaZero Solving Problems and Rule Variations

 Sharing variations of a problem.   All problems have variations, which specify their context, can this give us a hint for other solutions?

Reimagining Chess with AlphaZero

By Nenad Tomašev, Ulrich Paquet, Demis Hassabis, Vladimir Kramnik

Communications of the ACM, February 2022, Vol. 65 No. 2, Pages 60-66 10.1145/3460349

Modern chess is the culmination of centuries of experience, as well as an evolutionary sequence of rule adjustments from its inception in the 6th century to the modern rules we know today.17 While classical chess still captivates the minds of millions of players worldwide, the game is anything but static. Many variants have been proposed and played over the years by enthusiasts and theorists.8,20 They continue the evolutionary cycle by altering the board, piece placement, or the rules—offering players "something subtle, sparkling, or amusing which cannot be done in ordinary chess."1

Key Insights

Technological progress is the new driver of the evolutionary cycle. Chess engines increase in strength, and players have access to millions of computer games and volumes of opening theory. Consequently, the number of decisive games in super-tournaments has declined, and it takes longer for players to move from home preparation to playing original moves on the board.14 While classical chess remains a fascinating game and is unlikely to ever fall out of fashion, alternative variants provide an avenue for more creative play. In Fischer random chess, the brainchild of former world champion Bobby Fischer, the initial position is randomized to counter the dominance of opening preparation in a game.7 One could consider not only entirely new ideas, but also reassess some of the newer additions to the game. For example, the "castling" move was only introduced in its current form in the 17th century. What would chess have been like had castling not been incorporated into the rules? Without recourse to repeating history, we reimagine chess and address such questions in silico with AlphaZero.25

AlphaZero is a system that can learn superhuman chess strategies from scratch without any human supervision.19,22 It represents a milestone in artificial intelligence (AI), a field that has ventured down the corridors of chess more than once in search of challenges and inspiration. Throughout the history of computer chess, the focus was on creating systems that could spar with top human players over the board.3 Computer chess has progressed steadily since the 1950s, with better-tuned evaluation functions and enhanced search algorithms deployed on increasingly more computational resources.2,3,9,13,18,24 Alan Turing already envisioned more in 1953 by asking, "Could one make a machine to play chess, and to improve its play, game by game, profiting from its experience?"27 Unlike its predecessors, AlphaZero learns its policy from scratch from repeated self-play games, answering the second part of Turing's question. The result is a unique approach to playing classical chess22 and a new era in the development of chess engines, as spear-headed by Leela Chess Zero.15

AlphaZero's ability to continually improve its understanding of the game, and reach superhuman playing strength in classical chess and Go,25 lends itself to the question of assessing chess variants and potential variants of other board games in the future. Provided only with the implementation of the rules, it is possible to effectively simulate decades of human experience in a day, opening a window into top-level play of each variant. In doing so, computer chess completes the circle, from the early days of pitting man vs. machine to a collaborative present of man with machine, where AI can empower players to explore what chess is and what it could become.11

Back to Top  ... ' 

AI and Legal Discovery

More developments for the textually and logic intense world of Law.  How much of legal tasks be replaced by systems? Following this.

Everlaw announces $62M Series C to continue modernizing legal discovery   By Ron Miller@ron_miller in TechCrunch

Everlaw is bringing modern data management, visualization and machine learning to eDiscovery, the process in which legal entities review large amounts of evidence to build a case. Today, the company announced a $62 million Series C investment.

CapitalG  (Alphabet’s growth equity investment fund) and Menlo Ventures led the round. Existing investors Andreessen Horowitz and K9 Ventures also participated. The startup has now raised $96 million, according to the company.

Everlaw  co-founder and CEO AJ Shankar says eDiscovery, which has been around for years, has become a classic big data problem. “We help legal professionals sift through huge volumes of evidence in lawsuits and investigations to find the smoking gun and the incriminating email,” Shankar told TechCrunch.

The software also helps teams of legal professionals work together and collaborate around this evidence. “Turns out that the law is incredibly collaborative, and we help these teams create a work product, and communicate and collaborate with each other in a system specifically built for the practice of law,” he explained.

He says this coordination is often done manually in spreadsheets with communication taking place via email, and even companies using legacy eDiscovery software are using systems designed in a time of lower data volumes.

The company offers a variety of tools to help humans locate the information they need to build a case. There is a search feature, of course, and data visualization tools including a timeline tool that helps pinpoint when key events happened. This can help lawyers direct researchers to find evidence within that critical period, greatly narrowing the focus of the search.

And the company also offers both supervised and unsupervised machine learning algorithms to help the team find specific bits of information. He acknowledges it will take human ingenuity working with the tool to find what you need. “No one’s handing you anything on a silver platter. It absolutely requires some detective work. It’s iterative and we have tools to help you with that,” he said.

Shankar stumbled into this area of technology when he was still a graduate student in computer science and a law firm came to his department looking for a technical expert. He ended up working with the firm for a couple of years, saw the kinds of technical challenges it faced, and decided to build some tooling to help..... "

Collaborative Information Seeking

An abstract, but the challenge is interesting.  " ... Information searches based on expert-seeking technology can prove time-consuming or unsuccessful if search terms do not turn up extrinsic identifiers in profiles and saved documents. In many such cases, knowledge brokers function as "humans in the loop," providing intrinsic enterprise knowledge to mediate between information seekers and expert sources--a fact that future collaborative information-seeking system designs should take into account. .... "   Similar to what the company Zakta has been  attempting at the practical level for years.

Pepsi Doing Technology and AI

Interesting look at Pepsi's Technology Advances

04/05/2023

Tech Transformation Podcast: PepsiCo’s Kate Garner On Consumer Insights’ Evolution

There may be no bigger priority for both retailers and consumer goods companies today than access to consumer insights, and PepsiCo has stood up its own data analytics practice known as Pepviz to seize this opportunity. In this episode of Tech Transformation, we’re talking with Kate Garner, SVP of marketing, demand accelerator, at PepsiCo about some of their latest findings, how the company's use of technology has evolved, and how it's helping them develop more collaborative retail relationships. 

Listen to learn: 

How the use of technology at PepsiCo has evolved over the last 20 years

How Pepviz is helping retailers leverage consumer insights for market share and revenue growth

How they share these insights across the PepsiCo organization 

How PepsiCo overcomes some of the challenges associated with data democratization

What’s next for Pepviz and what Garner sees as the future of retail

The potential of generative AI within consumer goods and retail

How PepsiCo’s use of technology has evolved: “From an insights perspective, we've gone from a world of deep longitudinal surveys, where you're getting a lot of stated purchase behavior, and moving more into a place where we're able to look at actual purchase behavior and link that then to get to that closed-loop measurement that's so critical, to be able to ensure we're delivering that return on investment. 

“And then taking it even a step further and getting down to individual store level. So when we think about the insights that we're able to attain, and transfer that then all the way down to an advantage that we have, which is having our sales associates in all the stores across the country, we’re able then to curate those insights in a very granular way to what's happening in their store.”

How they work with retailers: “We had a Midwestern retailer who was very interested in growing their carbonated soft drink business. And we were able to look across their footprint of stores, and first able to separate out where stores were excelling and where stores had opportunities. Then, within their footprint, we could look to seek and understand what are some of the differences? What are lookalike stores — and lookalike stores doesn’t necessarily mean that they're in close geographical proximity, but specifically, how do we look at the shoppers that are shopping that store? And then identify what's unique or differentiated about those stores, and how do we take the stores that are underperforming, build some of the insights and things that are happening within the stores with higher growth. 

“We were able to then go activate against those execution parameters. Exciting for the retailer, as exciting for us, we were able to deliver incremental growth for both of us, for the retailer to see a 16% lift in their carbonated soft drink business.”

How they leverage consumer insights across the company: “It's almost like a marketing campaign, and you need to break down the target audience into different personas. We have some within our organization who use this capability on a day-in and day-out basis, and so for those folks, we have training, we have webinars, we record them, we put them at their fingertips, and then we have experts, who would have an office-hour type capability to meet with some of our sales and category leaders to answer questions, all the way up to our senior executives, where we're looking to just curate and help inform them on the insights and the learnings.

"And so there, you'll see we have quarterly newsletters that we're putting into the inbox. If you go into Pepviz.com, you also can get those and sign up to see some of those newsletters. So really articulating for the marketplace what are the industry leading insights that we're seeing.”

The potential of generative AI in consumer goods and retail: “One of the areas that I see potential in, having led insights organizations here within PepsiCo, really migrating away from stated behavior. And so when I think about the ability for sentiment tracking, rather than just tracking words, being able to get into tone and understanding, or in search, and helping us articulate more clearly. Those are areas that excite me to think about how we experience those things differently and more nuanced. …

"I think that how generative AI takes off within the CPG and retail space will be dependent upon how they find solutions that connect to those territories of where we are already investing as an industry. So to the extent that they can tap into those areas and show progress in those spaces, I think it will take off quickly. Otherwise, it may be areas of the future.”   ... '

Pets and Machine Learning Interactions

In Pete Warden's Blog, interesting views.   Have seen some of that in my own menagerie of chatbots and responsive assistants.    But I think ultimately we will want assistant than amusement.  Machine learning is collaborative in the sense that it solves narrow problems.  So does a 'Push Button' model of tech.  So will a 'pet model' be attentive and responsive?  A neighbor has a guide dog, which has been trained to be more attentive and responsive, rather than pet.   Seems more the model we will see.

Why ML interfaces will be more like pets than machines

When I talk to people about what’s happening in deep learning, I often find it hard to get across why I’m so excited. If you look at a lot of the examples in isolation, they just seem like incremental progress over existing features, like better search for photos or smarter email auto-replies. Those are great of course, but what strikes me when I look ahead is how the new capabilities build on each other as they’re combined together. I believe that they will totally change the way we interact with technology, moving from the push-button model we’ve had since the industrial revolution to something that’s more like a collaboration with our tools. It’s not a perfect analogy, but the most useful parallel I can think of is how our relationship with pets differs from our interactions with machines.

To make what I’m saying more concrete, imagine a completely made-up device for helping around the house (I have no idea if anyone’s building something like this, so don’t take it as any kind of prediction, but I’d love one if anybody does get round to it!). It’s a small indoors drone that assists with the housework, with cleaning attachments and a grabbing arm. I’ve used some advanced rendering technology to visualize a mockup below: