And here again, I have got to say that we want to address in a slightly different manner. I think we want to address it so that customers are able to take full advantage of the elasticity of the cloud, and also they’re able to take full advantage of on-prem environments. And how we want to do it, we want to do it in such a way that it’s almost in a seamless way, in a seamless manner. They can manage the data from their private data centers, doing the cloud and get the best from both worlds.
Laurel: An interesting perspective there, but this also kind of requires different elements of the business to come in. So from a leadership perspective, what are some best practices that you’ve instituted or recommended to make that transition to better data management?
Bharti: Yeah, I would say I think the data journey starts with data planning, and which should not be done in a siloed manner. And getting it right from the onset is extremely, extremely important. And what you need to do here is at the beginning of your data planning, you’ve got to get all the stakeholders together, whether it’s your CIO, your business users, your CTOs. So this strategy should never be done in a siloed manner. And in fact, I do want to think about, highlight another aspect, which probably people don’t do very much is how do you even bring your partners into the mix? In fact, I do have an example here. Prior to joining Hitachi Vantara, I was a CTO, an air purifier company. And as we were defining our data strategy, we were looking at our Salesforce data, we were looking at data in our NetSuite, we were looking at the customer tickets, and we were doing all this to see how we can drive marketing campaigns.
And as I was looking at this data, I felt that something was totally missing. And in fact, what was missing was the weather data, which is not our data, which was third-party data. For us to design effective marketing campaigns, it was very important for us to have insights into this weather data. For example, if there are allergies in a particular region or if there are wildfires in a particular region. And that data was so important. So having a strategy where you are able to bring all stakeholders, all parts of data together and think from the beginning is the right thing to get started.
Laurel: And with big hairy problems and goals, there’s also this consideration that data centers contribute to an enterprise’s carbon emissions. Thinking about partnerships and modernizing data management and everything we’ve talked about so far, how can enterprises meet sustainability goals while also modernizing their data infrastructure to accommodate all of their historical and real-time data, especially when it comes from, as you mentioned, so many different sources?
Bharti: Yeah, I’m glad that you are bringing up this point because it’s very important not to ignore this. And in fact, with all the gen AI and all the things that we are talking about, like one fine-tuning of one model can actually generate up to five times the carbon emissions that are possible from a passenger car in a lifetime. So we’re talking about a huge, huge environmental effect here. And this particular topic is extremely important to Hitachi. And in fact, our goal is to go carbon-neutral with our operations by 2030 and across our value chain by 2050. And how we are addressing this problem here is kind of both on the hardware side and also on the software side. Right from the onset, we are designing our hardware, we are looking at end-to-end components to see what kind of carbon footprint it creates and how we could really minimize it. And in fact, once our hardware is ready, actually, it needs to pass through a very stringent set of energy certifications. And so that’s on the hardware side.
Now, on the software side, actually, I have just started this initiative where we are looking at how we can move to modern languages that are more likely to create less carbon footprint. And this is where we are looking at how we can replace our existing Java [code base] with Rust, wherever it makes sense. And again, this is a big problem we all need to think about and it cannot be solved overnight, but we have to constantly think about interface manner.
Laurel: Well, certainly are impressive goals. How can emerging technologies like generative AI, as you were saying before, help push an organization into a next generation of data infrastructure systems, but then also help differentiate it from competitors?
These robots know when to ask for help
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The Download: inside the first CRISPR treatment, and smarter robots
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
5 things we didn’t put on our 2024 list of 10 Breakthrough Technologies
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.