AGI is going to need to keep up with expanding human knowledge even in a post-AGI world.getty In today’s column, I address a prevalent assertion that after AI is advanced to becoming artificial general intelligence (AGI) there won’t be anything else for humans to teach AGI about. The assumption is that AGI will know everything that we know. Ergo, there isn’t any ongoing need or even value in trying to train AGI on anything else. Turns out that’s hogwash (misguided) and there will still be a lot of human-AI, or shall we say human-AGI, co-teaching going on. Let’s talk about it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). Heading Toward AGI And ASI First, some fundamentals are required to set the stage for this weighty discussion. There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here. We have not yet attained AGI. In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. AGI That Knows Everything A common viewpoint is that if we do attain AGI, the AGI will know everything that humans know. All human knowledge will be at the computational fingertips of AGI. In that case, the seemingly logical conclusion is that AGI won’t have anything else to learn from humans. The whole kit-and-kaboodle will already be in place. For example, if you find yourself idly interested in Einstein’s theory of relativity, no worries, just ask AGI. The AGI will tell you all about Einstein’s famed insights. You won’t need to look up the theory anywhere else. AGI will be your one-stop shopping bonanza for all human knowledge. Suppose you decided that you wanted to teach AGI about how important Einstein was as a physicist. AGI would immediately tell you that you needn’t bother doing so. The AGI already knows the crucial nature that Einstein played in human existence. Give up trying to teach AGI about anything at all since AGI has got it all covered. Period, end of story. Reality Begs To Differ There are several false or misleading assumptions underlying the strident belief that we won’t be able to teach AGI anything new. First, keep in mind that AGI will be principally trained on written records such as the massive amount of writing found across the Internet, including essays, stories, poems, etc. Ask yourself whether the written content on the Internet is indeed a complete capture of all human knowledge. It isn’t. There are written records that aren’t on the Internet and just haven’t been digitized, or if digitized haven’t been posted onto the Internet. The crux is that there will still be a lot of content that AGI won’t have seen. In a post-AGI world, it is plausible to assume that humans will still be posting more content onto the Internet and that on an ongoing basis, the AGI can demonstrably learn by scanning that added content. Second, AGI won’t know what’s in our heads. I mean to say that there is knowledge we have in our noggins that isn’t necessarily written down and placed onto the Internet. None of that brainware content will be privy to AGI. As an aside, many research efforts are advancing brain-machine interfaces (BMI), see my coverage at the link here, which will someday potentially allow for the reading of minds, but we don’t know when that will materialize and nor whether it will coincide with attaining AGI. Time Keeps Ticking Along Another consideration is that time continues to flow along in a post-AGI era. This suggests that the world will be changing and that humans will come up with new thoughts that we hadn’t conceived of previously. AGI, if frozen or out of touch with the latest human knowledge, will have only captured human knowledge that existed at a particular earlier point in time. The odds are that we would want AGI to keep up with whatever new knowledge we’ve divined since that initial AGI launch. Imagine things this way. Suppose that we managed to attain AGI before Einstein was even born. I know that seems zany but just go with the idea for the moment. If AGI was locked into only knowing human knowledge before Einstein, this amazing AGI would regrettably miss out on the theory of relativity. Since it is farfetched to try and turn back the clock and postulate that AGI would be attained before Einstein, let’s recast this idea. There is undoubtedly another Einstein-like person yet to be born, thus, at some point in the future, once AGI is around, it stands to reason that AGI would benefit from learning newly conceived knowledge. Belief That AGI Gets Uppity By and large, we can reject the premise that AGI will have learned all human knowledge in the sense that this brazen claim refers solely to the human knowledge known at the time of AGI attainment, and of which was readily available to the AGI at that point in time. This leaves a whole lot of additional teaching available on the table. Plus, the passage of time will further increase the expanding new knowledge that humans could share with AGI. Will AGI want to be taught by humans or at least learn from whatever additional knowledge that humans possess? One answer is no. You see, some worry that AGI will find it insulting to learn from humans and therefore will avoid doing so. The logic seems to be that since AGI will be as smart as humans are, the AGI might get uppity and decide we are inferior and couldn’t possibly envision that we have anything useful for the AGI to gain from. I am more upbeat on this posture. I would like to think that an AGI that is as smart as humans would crave new knowledge. AGI would be eager to acquire new knowledge and do so with rapt determination. Whether the knowledge comes from humans or beetles, the AGI wouldn’t especially care. Garnering new knowledge would be a key precept of AGI, which I contend is a much more logical assumption than would the conjecture that AGI would stick its nose up about gleaning new human-devised knowledge. Synergy Is The Best Course Would humans be willing to learn from AGI? Gosh, I certainly hope so. It would seem a crazy notion that humankind would decide that we won’t opt to learn things from AGI. AGI would be a huge boon to human learning. You could make a compelling case that the advent of AGI could incredibly increase the knowledge of humans immensely, assuming that people can tap into AGI easily and at a low cost. Envision that everyone with Internet access could seek out AGI to train them or teach them on whatever topic they so desired. Boom, drop the mic. In a post-AGI realm, the best course of action would be that AGI learns from us on an ongoing basis, and on an akin ongoing basis, we also learn from AGI. That’s a synergy worthy of great hope and promise. The last word on this for now goes to the legendary Henry Ford: “Coming together is a beginning; keeping together is progress; working together is success.” If humanity plays its cards right, we will have human-AGI harmony and lean heartily into the synergy that arises accordingly.

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The saying goes that 80% of data collected, stored and maintained by governments can be associated with geographical locations. Although never empirically proven, it illustrates the importance of location within data. Ever growing data volumes put constraints on systems that handle geospatial data. Common Big Data compute engines, originally designed to scale for textual data, need adaptation to work efficiently with geospatial data — think of geographical indexes, partitioning, and operators. Here, I present and illustrate how to utilize the Microsoft Fabric Spark compute engine, with the natively integrated ESRI GeoAnalytics engine# for geospatial big data processing and analytics. The optional GeoAnalytics capabilities within Fabric enable the processing and analytics of vector-type geospatial data, where vector-type geospatial data refers to points, lines, polygons. These capabilities include more than 150 spatial functions to create geometries, test, and select spatial relationships. As it extends Spark, the GeoAnalytics functions can be called when using Python, SQL, or Scala. These spatial operations apply automatically spatial indexing, making the Spark compute engine also efficient for this data. It can handle 10 extra common spatial data formats to load and save data spatial data, on top of the Spark natively supported data source formats. This blog post focuses on the scalable geospatial compute engines as has been introduced in my post about geospatial in the age of AI. Demonstration explained Here, I demonstrate some of these spatial capabilities by showing the data manipulation and analytics steps on a large dataset. By using several tiles covering point cloud data (a bunch of x, y, z values), an enormous dataset starts to form, while it still covers a relatively small area. The open Dutch AHN dataset, which is a national digital elevation and surface model, is currently in its fifth update cycle, and spans a period of nearly 30 years. Here, the data from the second, third, and forth acquisition is used, as these hold full national coverage (the fifth just not yet), while the first version did not include a point cloud release (only the derivative gridded version). Another Dutch open dataset, namely building data, the BAG, is used to illustrate spatial selection. The building dataset contains the footprint of the buildings as polygons. Currently, this dataset holds more than 11 million buildings. To test the spatial functions, I use only 4 AHN tiles per AHN version. Thus in this case, 12 tiles, each of 5 x 6.25 km. Totalling to more than 3.5 billion points within an area of 125 square kilometers. The chosen area covers the municipality of Loppersum, an area prone to land subsidence due to gas extraction. The steps to take include the selection of buildings within the area of Loppersum, selecting the x,y,z-points from the roofs of the buildings. Then, we bring the 3 datasets into one dataframe and do an extra analysis with it. A spatial regression to predict the expected height of a building based on its height history as well as the history of the buildings in its direct surroundings. Not necessarily the best analysis to perform on this data to come to actual predictions* but it suits merely the purpose of demonstrating the spatial processing capabilities of Fabric’s ESRI GeoAnalytics. All the below code snippets are also available as notebooks on github. Step 1: Read data Spatial data can come in many different data formats; we conform to the geoparquet data format for further processing. The BAG building data, both the footprints as well as the accompanied municipality boundaries, come in geoparquet format already. The point cloud AHN data, version 2, 3 and 4, however, comes as LAZ file formats — a compressed industry standard format for point clouds. I have not found a Spark library to read LAZ (please leave a message in case there is one), and created a txt file, separately, with the LAStools+ first. # ESRI - FABRIC reference: https://developers.arcgis.com/geoanalytics-fabric/ # Import the required modules import geoanalytics_fabric from geoanalytics_fabric.sql import functions as ST from geoanalytics_fabric import extensions # Read ahn file from OneLake # AHN lidar data source: https://viewer.ahn.nl/ ahn_csv_path = "Files/AHN lidar/AHN4_csv" lidar_df = spark.read.options(delimiter=" ").csv(ahn_csv_path) lidar_df = lidar_df.selectExpr("_c0 as X", "_c1 as Y", "_c2 Z") lidar_df.printSchema() lidar_df.show(5) lidar_df.count() The above code snippet& provides the below results: Now, with the spatial functions make_point and srid the x,y,z columns are transformed to a point geometry and set it to the specific Dutch coordinate system (SRID = 28992), see the below code snippet&: # Create point geometry from x,y,z columns and set the spatial refrence system lidar_df = lidar_df.select(ST.make_point(x="X", y="Y", z="Z").alias("rd_point")) lidar_df = lidar_df.withColumn("srid", ST.srid("rd_point")) lidar_df = lidar_df.select(ST.srid("rd_point", 28992).alias("rd_point"))\ .withColumn("srid", ST.srid("rd_point")) lidar_df.printSchema() lidar_df.show(5) Building and municipality data can be read with the extended spark.read function for geoparquet, see the code snippet&: # Read building polygon data path_building = "Files/BAG NL/BAG_pand_202504.parquet" df_buildings = spark.read.format("geoparquet").load(path_building) # Read woonplaats data (=municipality) path_woonplaats = "Files/BAG NL/BAG_woonplaats_202504.parquet" df_woonplaats = spark.read.format("geoparquet").load(path_woonplaats) # Filter the DataFrame where the "woonplaats" column contains the string "Loppersum" df_loppersum = df_woonplaats.filter(col("woonplaats").contains("Loppersum")) Step 2: Make selections In the accompanying notebooks, I read and write to geoparquet. To make sure the right data is read correctly as dataframes, see the following code snippet: # Read building polygon data path_building = "Files/BAG NL/BAG_pand_202504.parquet" df_buildings = spark.read.format("geoparquet").load(path_building) # Read woonplaats data (=municipality) path_woonplaats = "Files/BAG NL/BAG_woonplaats_202504.parquet" df_woonplaats = spark.read.format("geoparquet").load(path_woonplaats) # Filter the DataFrame where the "woonplaats" column contains the string "Loppersum" df_loppersum = df_woonplaats.filter(col("woonplaats").contains("Loppersum")) With all data in dataframes it becomes a simple step to do spatial selections. The following code snippet& shows how to select the buildings within the boundaries of the Loppersum municipality, and separately makes a selection of buildings that existed throughout the period (point cloud AHN-2 data was acquired in 2009 in this region). This resulted in 1196 buildings, out of the 2492 buildings currently. # Clip the BAG buildings to the gemeente Loppersum boundary df_buildings_roi = Clip().run(input_dataframe=df_buildings, clip_dataframe=df_loppersum) # select only buildings older then AHN data (AHN2 (Groningen) = 2009) # and with a status in use (Pand in gebruik) df_buildings_roi_select = df_buildings_roi.where((df_buildings_roi.bouwjaar<2009) & (df_buildings_roi.status=='Pand in gebruik')) The three AHN versions used (2,3 and 4), further named as T1, T2 and T3 respectively, are then clipped based on the selected building data. The AggregatePoints function can be utilized to calculate, in this case from the height (z-values) some statistics, like the mean per roof, the standard deviation and the number of z-values it is based upon; see the code snippet: # Select and aggregrate lidar points from buildings within ROI df_ahn2_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T1_z", statistic="Mean", alias="T1_z_mean") \ .addSummaryField(summary_field="T1_z", statistic="stddev", alias="T1_z_stddev") \ .run(df_ahn2) df_ahn3_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T2_z", statistic="Mean", alias="T2_z_mean") \ .addSummaryField(summary_field="T2_z", statistic="stddev", alias="T2_z_stddev") \ .run(df_ahn3) df_ahn4_result = AggregatePoints() \ .setPolygons(df_buildings_roi_select) \ .addSummaryField(summary_field="T3_z", statistic="Mean", alias="T3_z_mean") \ .addSummaryField(summary_field="T3_z", statistic="stddev", alias="T3_z_stddev") \ .run(df_ahn4) Step 3: Aggregate and Regress As the GeoAnalytics function Geographically Weighted Regression (GWR) can only work on point data, from the building polygons their centroid is extracted with the centroid function. The 3 dataframes are joined to one, see also the notebook, and it is ready to perform the GWR function. In this instance, it predicts the height for T3 (AHN4) based on local regression functions. # Import the required modules from geoanalytics_fabric.tools import GWR # Run the GWR tool to predict AHN4 (T3) height values for buildings at Loppersum resultGWR = GWR() \ .setExplanatoryVariables("T1_z_mean", "T2_z_mean") \ .setDependentVariable(dependent_variable="T3_z_mean") \ .setLocalWeightingScheme(local_weighting_scheme="Bisquare") \ .setNumNeighbors(number_of_neighbors=10) \ .runIncludeDiagnostics(dataframe=df_buildingsT123_points) The model diagnostics can be consulted for the predicted z value, in this case, the following results were generated. Note, again, that these results cannot be used for real world applications as the data and methodology might not best fit the purpose of subsidence modelling — it merely shows here Fabric GeoAnalytics functionality. R20.994AdjR20.981AICc1509Sigma20.046EDoF378 Step 4: Visualize results With the spatial function plot, results can be visualized as maps within the notebook — to be used only with the Python API in Spark. First, a visualization of all buildings within the municipality of Loppersum. # visualize Loppersum buildings df_buildings.st.plot(basemap="light", geometry="geometry", edgecolor="black", alpha=0.5) Here is a visualization of the height difference between T3 (AHN4) and T3 predicted (T3 predicted minus T3). # Vizualize difference of predicted height and actual measured height Loppersum area and buildings axes = df_loppersum.st.plot(basemap="light", edgecolor="black", figsize=(7, 7), alpha=0) axes.set(xlim=(244800, 246500), ylim=(594000, 595500)) df_buildings.st.plot(ax=axes, basemap="light", alpha=0.5, edgecolor="black") #, color='xkcd:sea blue' df_with_difference.st.plot(ax=axes, basemap="light", cmap_values="subsidence_mm_per_yr", cmap="coolwarm_r", vmin=-10, vmax=10, geometry="geometry") Summary This blog post discusses the significance of geographical data. It highlights the challenges posed by increasing data volumes on Geospatial data systems and suggests that traditional big data engines must adapt to handle geospatial data efficiently. Here, an example is presented on how to use the Microsoft Fabric Spark compute engine and its integration with the ESRI GeoAnalytics engine for effective geospatial big data processing and analytics. Opinions here are mine. Footnotes # in preview * for modelling the land subsidence with much higher accuracy and temporal frequency other approaches and data can be utilized, such as with satellite InSAR methodology (see also Bodemdalingskaart) + Lastools is used here separately, it would be fun to test the usage of Fabric User data functions (preview), or to utilize an Azure Function for this purpose. & code snippets here are set up for readability, not necessarily for efficiency. Multiple data processing steps could be chained. References GitHub repo with notebooks: delange/Fabric_GeoAnalytics Microsoft Fabric: Microsoft Fabric documentation – Microsoft Fabric | Microsoft Learn ESRI GeoAnalytics for Fabric: Overview | ArcGIS GeoAnalytics for Microsoft Fabric | ArcGIS Developers AHN: Home | AHN BAG: Over BAG – Basisregistratie Adressen en Gebouwen – Kadaster.nl zakelijk Lastools: LAStools: converting, filtering, viewing, processing, and compressing LIDAR data in LAS and LAZ format Surface and Object Motion Map: Bodemdalingskaart – The post The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated appeared first on Towards Data Science.

As the Muppets’ presence at Disney Parks continues to evolve, the beloved Jim Henson Company characters are marking their 70-year anniversary at the same time as Disneyland. With that in mind Disney Experiences and Muppet Studios collaborated on a special show to fit perfectly along with the celebrations across the Disneyland Resort. io9 was present for the premiere of the Muppets’ special greeting before the first showing of World of Color: Happiness, the nighttime water spectacular at Disney California Adventure. Before the show, representatives and producing teams from Disney Experiences discussed the latest collaboration between Disney and Henson. “Well, there’s a lot of hijinks I can tell you that for sure,” said Susanna Tubert, creative director of Disney Live Entertainment. “But it quickly became apparent to us that the Muppets are actually celebrating their 70th anniversary as well. So when we reached out to our partners at the Muppet Studios and said, ‘Hey, do the Muppets want to play with us? It was also resounding: yes.” Tubert added, “I will share that Miss Piggy had a lot of requests, there were a lot of wardrobe requests. There are a few special outfits that may have been made for her for this. Gonzo got into some hijinks [and] it all kind of went rolling down hill very quickly, but we had a lot of fun.” © io9 Gizmodo The Muppets moment before World of Color presents some classic Muppet Show-style shenanigans, as they’re there to give audiences instructions on how to spectate the show they’re about to watch. All of the iconic characters get in on the action and it makes you wish they’d just run the rest of the show. There’s music and classic gags. Be sure to watch out for Gonzo the Great making a death-defying jump over the water, and of course there’s enough Mayhem to go around with Dr. Teeth and friends. What’s interesting is that World of Color: Happiness heavily features Paul Williams’ iconic Muppets tune “Rainbow Connection” covered by Boys II Men—but the characters themselves don’t appear in the show, which is such a wasted opportunity. Inside Out fans will be happy to know all of Riley’s emotions handle the mix-tape tinged new water show that fits the chaotic tone that takes you on a real rollercoaster of feelings with new and nostalgic Disney musical moments. But we wanted more Muppets! We lost their 3D show in California years ago and Disney World’s version just followed suit. While we look forward to the Muppets coaster on the East Coast, we hope the West Coast isn’t forgotten. The World of Color intro got some good cheers and showed how even projected on water, the Muppets energy cannot be contained. And as they celebrate 70 years of timelessness, that is something we shouldn’t take for granted. World of Color: Happiness with a special message from the Muppets will be projected on scheduled nights during Disneyland’s 70th Anniversary at the Disneyland Resort. Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.

At least Ghost Of Yōtei is out this year (Sony Interactive Entertainment) The Thursday letters page worries what will happen if the new Marvel games are flops, as one reader laments the slow death of physical games. To join in with the discussions yourself email gamecentral@metro.co.uk Summer schedule I really hope that Sony is planning to make more first party games but I have a feeling there’s no going back to how things were. It’s been too long and too much has changed, including Sony still selling tons of PlayStation 5s even without any proper exclusives and Xbox going multiformat.I think it’s reasonable to hope they will do more though. We can’t stop them making any more live service games but they do seem to at least be slowing down a bit. The problem is that slowing up doesn’t mean they’ll speed up with single-player games instead. They could just end up making less overall, like they have over the last few years. We’ve got Ghost Of Yōtei this year, which I’m not super excited about (I’d had enough of the last one by halfway through) but I am very interested in seeing Returnal follow-up Saros next year, so hopefully things are slowly turning around. I want to be optimistic, but the problem is there’s no sign of Sony having a not-E3 State of Play showcase this summer. The past two years they’ve had it at the end of May and I think we’ve probably gone past the point they were going to announce it if it was happening. I hope I’m wrong, or maybe it’s later in June for some reason, but it doesn’t look good.RaeBonn Your story is another castle I see the Super Mario Bros. Movie sequel has had its name leaked as Super Mario World. I’m not sure your average Joe is going to get that reference, but would they actually bother doing an adaptation of the game’s story? Does the game even have a story? I’ve beaten it twice and I honestly can’t remember.Looking at Wikipedia, apparently it was all in the manual and basically Bowser kidnapped Peach and some dinosaur eggs. There’s so little to work with I really have no idea what direction any of these films could do to justify themselves as anything other than a cash-in. The Paper Mario games are the only ones with an actual plot, but even then that’s not what’s good about them.Austin Risky business I’ve got a bad feeling about these upcoming Marvel games. I imagine Wolverine will be fine, but that Iron Man game will be almost two decades too late by the time it comes out. And I can’t believe many people are interested in a Captain America and Black Panther team-up set in World War 2.Normally I’d just shrug my shoulders and write it off as an incoming flop but nowadays that means the whole studio could go down and how many hundreds of people are working on these two games? I’m not exaggerating either. Consider what happened to Firaxis after the (excellent) Midnight Suns flopped. All the lead developers left, the studio was fractured (I don’t think it was a coincidence that Civilization 7 was a rare dud), and now we’ll never get an XCOM 3. I don’t know these developers behind Iron Man and the other one as well, but I do know that lots of jobs are now in danger, just because EA was late jumping on a bandwagon.Cerates Email your comments to: gamecentral@metro.co.uk Reliable reputation Glad to hear GC got Power Stone back and I like the sound of all the effort Capcom put into the collection and the reasonable price. I’ve never really been into fighting games, so it’s not really of interest to me. But it Does make me think better of Capcom, for when something else I am interested in comes up.I wish this was something other publishers thought of, especially as Capcom is doing so well, financially speaking, at the moment. But unfortunately EA and Activision, and even Ubisoft to a degree, just rely on their big franchises and don’t really put out much else anyway. Sony is another example of doing this well, because their reputation for first party games really wasn’t that great until the PlayStation 4. They just built it up and became someone to rely on. Them throwing that all away now is so strange.Griefer Money issue I’ve been thinking for a while now about this, but Sony could take Xbox out the console market with just one sentence. You can put Game Pass on PlayStation. There wouldn’t be any need for an Xbox and we know Phil wants it on there.Why Sony won’t do that I’ll never know. It’s a win/win for Sony. You never know, Sony might allow them sooner or later and Xbox can go the way of Sega, just with a hell of a lot more money and just to make games.David GC: They won’t do it because they make more money selling games on their store. Although they may change their mind in time, because they’ve begun to make more selling microtransactions than actual games. And another thing You know, I have been witnessing quite a bit of clamour over the last few days. More or less a lot of complaining and quite a stir of negativity. I’ve seen GTA 6 fall surprisingly prey to this. Either people have issues with the inevitably violent content, the sex or the aged fable of controversy. My take is my bond and I say that I gladly welcome the gratuitous violence, the sex, and the glory glitz of Leonida.Because I’ve waited over 12 years for an evolution and no echoes of bemusement will deter me from my course of securing the collector’s edition as soon as possible. If that includes a statue or early access I will be immensely satisfied. My second point is the Nintendo Switch 2. I’ve already mentioned that I secured the Mario Kart World bundle from Amazon and I acknowledge the bricking controversy. But modding isn’t my cup of tea, so Nintendo has nothing to worry about. I’d rather emulate PlayStation 2 games anyway. Thirdly, the PlayStation 6. It matters not the price, storage, exclusives or specs. I’m buying this system, as I did the PlayStation 5. Day one. If it arrives in 2027, the merrier it is for me. I will very much take great stride in playing Horizon Zero Dawn 3 and many other titles.Shahzaib Sadiq Mum’s the word I was wondering when we might get some concrete thoughts about the Switch 2 launch games?Is there an embargo about when Switch 2 games are able to be reviewed? Or perhaps there is an embargo on when you can talk about an embargo, nothing from Nintendo would surprise me.Mark GC: There’s always an embargo on when we can talk about an embargo, but especially when it involves Nintendo. Facing defeat I know the war was lost long ago (shops stopping second-hand games was the end, as far as I see it) but it is so tragic to see how small a part of gaming physical copies are now. I don’t know anyone, amongst my friends or family, that buys physical copies anymore and what would be the point anyway, when they’re not even stored on the disc?.Perhaps it was inevitable, since even if the initial game is on the disc it gets distorted and expanded by updates the second you put it in a console but it’s the fact that there’s literally no benefit to digital. People talk about convenience but what convenience? Not having to change discs is just silly and lazy. And it takes time to buy a physical copy? I could drive to the shops and back quicker that it takes me to download a modern game. Not that I’m in the habit of paying out £60 on a whim, that I suddenly decide I have to have a new game right then and there. Gaming got infinitely more expensive as soon as it became more difficult to sell on your games, especially with eBay’s new payment rules. But apparently nobody cares. Maybe everyone else is a millionaire all of a sudden, and I didn’t get the memo, but the death of physical is just handing more control to publishers and taking more cash out of the wallets of ordinary people.Rufus Inbox also-rans Just watched the Borderlands movie on Amazon. It’s no Fallout or The Last Of Us but it’s not that bad. I particularly liked Jack Black as the annoying android Claptrap. They should make a Brütal Legend movie – it would have a great soundtrack. Think I will check out the Like A Dragon movie next.Johnny Alpha SDCurrently playing: Doom Eternal and Call Of Duty: Black Ops 6Thanks for the heads-up on Very, I managed to get my bundle with a camera so I’m very excited now. I went back and checked and it’s still live though, which seems weird given no one else has any stock at all.Boysie More Trending Email your comments to: gamecentral@metro.co.uk The small printNew Inbox updates appear every weekday morning, with special Hot Topic Inboxes at the weekend. Readers’ letters are used on merit and may be edited for length and content. You can also submit your own 500 to 600-word Reader’s Feature at any time via email or our Submit Stuff page, which if used will be shown in the next available weekend slot. You can also leave your comments below and don’t forget to follow us on Twitter. Arrow MORE: Games Inbox: Is it weird to not like GTA games? GameCentral Sign up for exclusive analysis, latest releases, and bonus community content. This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply. Your information will be used in line with our Privacy Policy

Chicago, like many U.S. cities, has a history of building public housing and then tearing it down. Cabrini-Green, a textbook example, was demolished decades ago, along with so many other storied campuses around the country like Pruitt-Igoe in St. Louis. The National Public Housing Museum (NPHM) opened its doors last month in Chicago’s near West Side to help tell this multivalent, often times tumultuous story. It’s sited at 919 South Ada Street inside the last remaining vestige of Jane Addams Homes, a New Deal–era campus by Holabird & Root. The institution is the first of its kind. NPHM was founded by public housing residents—its goal is to become “a place to experience stories of hope and personal achievement amid struggle, resistance, and resilience,” NPHM said in a statement. WPA posters at the National Public Housing Museum (Barry Brecheisen) AN first reported on the project in 2016, when the Chicago Housing Authority approved the redevelopment of the site. Landon Bone Baker Architects (LBBA) oversaw the adaptive reuse effort, shepherded by Peter Landon. The campaign took 18 years to complete from start to finish—the most noticeable changes on the exterior are new signage, painted onto the brick facade, and a new entryway. Sunny Fischer cofounded NPHM and now sits on the board. She grew up in the Bronx at a public housing campus. Fischer called NPHM a Site of Conscience, or a space to remember and address past injustices and their ongoing legacies. “As a Site of Conscience, we join museums around the world committed to telling complicated and difficult stories, preserving history, and imagining a more just future,” Fischer said. View Inside the Turovitz Apartment (Barry Brecheisen) Upon entry, visitors can see illustrative WPA advertisements for public housing, bygone relics from a time when the federal government invested in such things. Three historic apartments were reconstructed at a 1:1 to scale to show how different generations of public housing residents lived. Instead of your conventional gift shop, the museum store is co-operatively run by public housing residents. NPHM also has a REC Room, a curated space by DJ Spinderella showcasing the beats and melodies born on public housing campuses. The Doris Conant Demand the Impossible Advocacy Space is meant to encourage discussions about social justice. Art by Amanda Williams, Olalekan Jeyifous, and Alphawood Foundation Sculpture Garden animates the building, together with WPA-era Animal Court sculptures by Edgar Miller. In the REC Room, records are on display in wood shelves. (Percy Ollie Jr. of Ollie Photography) Ephemera on display throughout NPHM speaks to the cultural, social, and economic dimensions of life in public housing. (Barry Brecheisen) Francine Washington grew up in public housing and is now a chairperson of the Central Advisory Committee for the Chicago Housing Authority. “This museum is personal,” Washington added. “It’s the first in the country to tell our stories—not just the bricks and buildings but the people who made public housing, home. We’ve always had a voice. Now we have a place that listens.” NPHM executive director Dr. Lisa Yun Lee said the longterm mission is solidifying housing’s place as a human right, not a commodity. Lee envisions it as an “important civic anchor that brings people together to imagine innovative solutions and envision a more equitable future.”

A sketchy AI firm tried to pass off a bogus Steam breach, but it unraveled almost immediately. This one was a fake, but the next one might not be. Here's how to protect yourself from losing control of an account that may be worth thousands of dollars.Steam appA recent claim on LinkedIn alleges that a database containing 89 million Steam account records, including one-time passcodes (OTPs) used for two-factor authentication (2FA), is up for sale. The asking price is $5,000, a low figure for a leak of this scale.But despite the headline-grabbing figure and some reposts online, the evidence supporting this leak was outright fabricated. Fortunately, Apple users can take advantage of the built-in Passwords app, which now supports two-factor codes across iPhone, iPad, and Mac. Continue Reading on AppleInsider | Discuss on our Forums

In the world of entity resolution (ER), one of the central challenges is managing and maintaining the complex relationships between records. At its core, Tilores models entities as graphs: each node represents a record, and edges represent rule-based matches between those records. This approach gives us flexibility, traceability, and a high level of accuracy, but it also comes with significant storage and computational challenges, especially at scale. This article explains the details about efficiently storing highly connected graphs using clique-based graph Compression. The Entity Graph Model In Tilores, a valid entity is a graph where every record is connected to at least one other via a matching rule. For instance, if record a matches record b according to rule R1, we store that as an edge "a:b:R1". If another rule, say R2, also connects a and b, we store an additional edge "a:b:R2". These edges are kept as a simple list, but could alternatively be modeled using an adjacency list structure for more efficient storage. Why Retain All Edges? Most Entity Resolution systems or master data management systems don’t retain the relationships between records, but only store a representation of the underlying data and typically a generic matching score, leaving the user uncertain about how the entity was formed. Even worse, the user has no way to correct errors made by the automatic matching system. Hence, retaining all edges in an entity graph serves multiple purposes: Traceability: Allows the user to understand why two records were grouped into the same entity. Analytics: Insights such as rule effectiveness and data similarity can be extracted from edge metadata. Data Deletion & Recalculation: When a record is deleted or a rule is modified, the graph must be recalculated. Edge information is essential to understand how an entity was formed and how it should be updated. The Scaling Problem: Quadratic Growth When discussing potential scaling issues in entity resolution, this typically refers to the challenge of matching each record with all other records. While this is a challenge on its own, retaining all edges of an entity results in similar issues on the storage side. Entities where many records are connected with each other create a multitude of edges. In the worst case every new record is connected to all existing records. This quadratic growth can be expressed with the formula: n * (n - 1) / 2 For small entities, this is not an issue. For example, an entity with 3 records can have a maximum of 3 edges. For n = 100, this increases to 4,950 edges and for n = 1,000 this results in up to 499,500 edges. This creates an immense storage and computational overhead, especially since entity resolution graphs often exhibit this kind of dense connectivity. Solution: Clique-Based Graph Compression (CBGC) A clique in a graph is a group of nodes where every node is connected to every other node in that group. A clique can also be called a complete subgraph. The smallest possible clique contains a single node and no edges. A pair of nodes connected by an edge also forms a clique. And three nodes, such as the one below, form a triangle shaped clique. A Simple Clique: Triangle(image by the author) A maximal clique is a clique that cannot be extended by adding any adjacent node, and a maximum clique is the clique with the largest number of nodes in the whole graph. For the purpose of this article, we’re going to use the term clique only to refer to cliques with at least three nodes. The previously shown triangle could be represented in Tilores by the following edges: [ "a:b:R1", "a:c:R1", "b:c:R1" ] Because a triangle is a clique, we could also represent the graph by storing only the nodes in this clique and the associated rule ID: { "R1": [ ["a", "b", "c"] ] } Let’s consider the following slightly more complicated graph: Complete Subgraph with 6 Nodes(image by the author) Based on its appearance, we can easily spot that all nodes are connected to each other. So instead of listing all 15 edges [remember n*(n-1)/2], we can simply store this clique in the following form: { "R1":[ ["a", "b", "c", "d", "e", "f"] ] } However, in a realistic graph, not all records are connected to each other. Consider the following graph: A Complex Graph with Three Highlighted Cliques(image by the author) There are three larger cliques highlighted: yellow, red and blue (teal if you’re picky). There is also a single remaining node. While those are probably the largest cliques, you might spot dozens of others. For example, do you notice the 4-node clique between the two red and two yellow nodes? Sticking with the colored cliques, we could store them in the following way (using y, r and b for yellow, red and blue): { "R1": [ ["y1", "y2", "y3"], ["r1", "r2", "r3", "r4", "r5"], ["b1", "b2", "b3", "b4", "b5", "b6"] ] } Additionally, we can store the remaining 10 edges (p for purple): [ "y1:r1:R1", "y1:r2:R1", "y2:r1:R1", "y2:r2:R1", "r4:p1:R1", "r5:p1:R1", "r5:b1:R1", "b2:p1:R1", "y3:b5:R1", "y3:b6:R1" ] This means that the whole graph can now be represented with only three cliques and ten edges, instead of the original 38 edges. Compressed Graph(image by the author) This clique-based graph compression (CBGC) is loss-free (unless you need edge properties). In a realistic dataset, we identified massive storage savings. For one customer, CBGC reduced edge storage by 99.7%, replacing hundreds of thousands of edges with just a few hundred cliques and sparse edges. Performance Benefits Beyond Storage CBGC is not just about compression. It also enables faster operations, particularly when handling record and edge deletion. Any sane entity resolution engine should split an entity into multiple ones if the only link between two subgraphs was deleted, for example, due to regulatory or compliance reasons. Identifying separate, unconnected subgraphs is typically done using a connected components algorithm. In short, it works by grouping all nodes that are connected via edges into separate subgraphs. As a result each edge needs to be checked at least once. However, if a graph is stored as a compressed graph, then there is no need to traverse all edges of a clique. Instead it is sufficient to add a limited number of edges for each clique, for example a transitive path between the nodes of a clique, treating each clique as a pre-connected subgraph. Trade-Offs: Clique Detection Complexity There is a trade-off: clique detection is computationally expensive, particularly when attempting to find the maximum cliques, a well-known NP-hard problem. In practice it is often sufficient to simplify this workload. Approximate algorithms for clique detection (e.g. greedy heuristics) perform well enough for most uses. Additionally, CBGC is recalculated selectively, usually when an entity’s edge count surpasses a threshold. This hybrid approach balances compression efficiency with acceptable processing cost. Beyond Cliques Arguably, the most common pattern in entity resolution is the complete subgraph. However, further optimization could be achieved by identifying other recurring patterns such as stars: store as a list of nodes where the first entry represents the central node paths: store as an ordered list of nodes communities: store like a clique and mark the missing edges Closing Thoughts Entity resolution systems often face the challenge of managing dense, highly interconnected graphs. Storing all edges naively quickly becomes unsustainable. CBGC provides an efficient way to model entities by exploiting structural properties of the data. Not only does it reduce storage overhead, but it also improves system performance, especially during data deletion and reprocessing. While clique detection has its computational costs, careful engineering choices allow us to reap the benefits without sacrificing scalability. The post Efficient Graph Storage for Entity Resolution Using Clique-Based Compression appeared first on Towards Data Science.

Evening star For the first time in the US, a rotating detonation rocket engine takes flight "Hypersonics is one of the critical technologies to remain ahead of our national competitors." Eric Berger – May 14, 2025 6:47 pm | 23 A rotating detonation rocket engine takes off in New Mexico. Credit: Venus Aerospace A rotating detonation rocket engine takes off in New Mexico. Credit: Venus Aerospace Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more A US-based propulsion company, Venus Aerospace, said Wednesday it had completed a short flight test of its rotating detonation rocket engine at Spaceport America in New Mexico. The company's chief executive and co-founder, Sassie Duggleby, characterized the flight as "historic." It is believed to be the first US-based flight test of an idea that has been discussed academically for decades, a rotating detonation rocket engine. The concept has previously been tested in a handful of other countries, but never with a high-thrust engine. "By proving this engine works beyond the lab, Venus brings the world closer to a future where hypersonic travel—traversing the globe in under two hours—becomes possible," Duggleby told Ars. A quick flight The company has only released limited information about the test. The small rocket, powered by the company's 2,000-pound thrust engine, launched from a rail in New Mexico. The vehicle flew for about half a minute, and, as planned, did not break the sound barrier. Governments around the world have been interested in rotating detonation engine technology for a long time because it has the potential to significantly increase fuel efficiency in a variety of applications, from Navy carriers to rocket engines. In contrast to a traditional rocket engine, in which a highly pressurized propellant and an oxidizer are injected into a combustion chamber where they burn and produce an energetic exhaust plume, a rotating detonation engine is different in that a wave of detonation travels around a circular channel. This is sustained by the injection of fuel and oxidizer and produces a shockwave that travels outward at supersonic speed. Sassie Duggleby and her husband, Andrew Duggleby, founded Venus Aerospace nearly five years ago with the long-term goal of developing powerful rotating detonation engines and building a hypersonic aircraft that could carry perhaps a dozen passengers and travel at astonishingly fast speeds worldwide. Aiming for hypersonics in the near term Such a prospect is many years into the future, however. In the meantime, Venus is looking at more near-term opportunities and revenue sources. The company is seeking to position itself as a leader in affordable hypersonic flight for commercial and defense applications. "It is vital for US interests that the government continues to fund efforts to maintain America’s economic competitiveness and national security," Sassie Duggleby said. "Hypersonics is one of the critical technologies to remain ahead of our national competitors. We’re fortunate to have robust interest from both government and commercial sectors." Sassie Duggleby said Venus cannot disclose specific customers yet. "We're actively engaged with US defense and national security agencies as well as commercial partners exploring hypersonic applications in logistics, aerospace, and future mobility—including large primes," she said. "The enthusiasm we’re seeing reflects a broad recognition that Venus’s technology can unlock new operational and economic possibilities across multiple markets." Venus has a long way to go to reach its ultimate goal of highly efficient, hypersonic commercial flight. But the engine that flew this week is a good start toward proving that such a future, at least, is possible. Eric Berger Senior Space Editor Eric Berger Senior Space Editor Eric Berger is the senior space editor at Ars Technica, covering everything from astronomy to private space to NASA policy, and author of two books: Liftoff, about the rise of SpaceX; and Reentry, on the development of the Falcon 9 rocket and Dragon. A certified meteorologist, Eric lives in Houston. 23 Comments