Without question, AI is primed to change the nature of how we work, with the potential to help businesses unlock $16 trillion in value by 2030. This means enterprises will need to reckon with how best to leverage this tool in the future. However, like other revolutionary technologies, the printing press and the internet among them, there’s plenty of potential for misuse as well.

While previous redefining technologies like the printing press, the internet and social media, have advanced the way we work, play, learn and connect, these technologies have also needed updated policies when threatened by improper practices. In this respect, AI is no different. The flip side to knowledge sharing, education, journalism and the general spread of novel ideas means AI may be vulnerable to disinformation, bias and inaccuracies.

While poised to revolutionize all facets of society, artificial intelligence is rife with both cautious optimism and skepticism from consumers and businesses leaders alike. Consumers often don’t realize how much they are already using AI in their day to day lives. A 2023 PEW Research survey which found that 30% of Americans, when asked to recognize up to six forms of AI, including wearable tech and product recommendations, couldn’t do so. So how do we bridge the gap between AI understanding and AI adoption? As this technology evolves, acceptance may seem a prudent first step. A step that also requires collaboration. 

“So if you look at the ecosystem of AI, it transcends from technology companies, software and hardware, to application developers to academia, to startups, to national labs across the globe. This is the ecosystem that we mean, that we need AI to be open” says VP of IBM Research AI, Sriram Raghavan. This sentiment is at the heart of the AI Alliance, an international community of more than 100 organizations bringing together top AI scientists, researchers, policy experts, business leaders, and innovators focused on accelerating and disseminating open innovation across the AI technology landscape.

Like any technology, AI is a tool. It can help individuals and enterprises alike to create value by doing away with labor intensive tasks. Raghavan elaborates on this and says, “at its core, AI as a technology is about two things, getting insights out of data, and automation.” By offloading laborious tasks, businesses can create value for their customers in other ways, like developing new products or offering a more personal approach with clients. It’s about making sense of data and doing automation or improving productivity. By making sense of that data, businesses hope to convert that comprehension into actual dollars.

“An open ecosystem has an opportunity to influence the way this technology evolves,” says Raghavan. “All of the stakeholders in this ecosystem are able to participate in the creation, adoption, benchmarking, and evaluation of models. The development of AI technologies should not be determined only by the few. Whenever you have a profound technology, it’s critical that its use and development is unconstrained in the open.”

With the acceleration of innovation, open models will be a defining force in the evolution of AI technology now and in the future. That’s why in collaboration with Red Hat, IBM recently launched InstructLab – a new and unique open-source project which places large language model (LLM) development into the hands of the open-source developer community. Collectively, that community can contribute new skills, knowledge and information to any LLM. Going further, IBM open-sourced a family of its most capable Granite models  which help transform and optimize applications for enterprises. The goal with open-sourcing these models is to make tasks such as coding as easy and accessible as possible, for as many developers as possible.

The implications of AI are many, from enterprises to the individual or end user of the technology. Taking this into consideration, if the printing press or the internet had only been in the hands of a few companies or institutions, the world as we know it would look very different. Burgeoning technology like AI is crucial for businesses, and people alike, to acknowledge, identify and learn about its plethora of capabilities. The impacts of this technology may be incredibly far reaching, to which Raghavan adds “For a technology as profound and far reaching, the critical aspect is open participation. The key is to make sure that all of the elements, the creation, adoption, evaluation, benchmarking and safety of the model, is then done in an open and transparent fashion. That is the open ecosystem for AI that we’re talking about.”

As we interact and engage with the world around us, including objects of desire, our senses act as the entryway to our brains and bodies. The senses have a major role to play in revving our emotions and motivations. Considering this, can an all-new Alfa Romeo Tonale engage our senses?

“The human brain’s neural circuitry is dedicated to survival, first and foremost. Our biology is really designed and gamed for short term survival. Which is why we are so pleasure seeking by nature.” says Dr. Sahar Yousef, cognitive neuroscientist at UC Berkeley. Dr. Yousef goes on to explain “The relationship between emotion and the pursual of reward is really fascinating. The bigger the emotional response, the more likely it is to actually impact and influence future action, future desire, future pursuit of reward.” 

Humans are motivated by rewards, and our senses act as a bridge between our experiences which in turn promote behaviors that seek said rewards. Sitting in the driver’s seat of a Tonale, our senses are engaged. As mechanoreceptors in our muscles and skin feel the power of the engine, or olfactory receptors relay the aroma of that new car smell, it all funnels back to the reward center. 

The reward system in the brain is made up of the limbic system, a set of neural structures that manage motivation, emotion, learning and memory. So when we engage an object of desire, the limbic system evaluates that desire, with help from neuromodulators like dopamine, then prompts us to engage in behaviors that seek what feels good and avoid what doesn’t. In this case, the reward could be to test drive or buy a Tonale. 

“It motivates pursuit, pursuit towards action, pursuit towards things that feel good, and also help keep us alive, so that we are surviving and thriving” says Dr. Yousef. When not taken to extremes, hedonism has a vital part to play in human evolutionary progress. Whether that be acquiring a new skill, or having altogether new experiences, experiencing through the senses and pleasure seeking, conditions humans to seek rewards again and again.

Hedonism historically has gotten a bad rap, when taken to extremes, but the pursuit of rewards has also helped humans progress and evolve, so a little indulgence can’t be all that bad, right? Perhaps we should take that Tonale for a spin after all.

As the global population is expected to balloon to 10 billion people by 2050, communities around the globe will need to be better prepared to protect their people, so they’ll need more accurate flood detection and modeling. To do exactly that, IBM Research is developing a Global Climate Network, which uses AI-enabled accelerated discovery, to predict floods before they happen. 

What is a Climate Network? This network is a system of computing systems, where highly distributed climate information and services from across the globe become discoverable or accessible.  Participants can share information across the network, and this sharing enables seamless federation and AI modeling to accelerate.  That collaborative practice can lead to the development of climate adaptation and mitigation solutions. So what does AI modeling have to do with flood prevention?

In 2021, IBM Research introduced a method that could predict daily precipitation of a region up to six months into the future. “The Climate Network is really doing three things: it is accelerating the discovery of climate information. It accelerates the developments of AI models. And then finally, it accelerates the collaboration” says Chief Scientist for Climate and Sustainability Hendrik Hamann. Critical to this effort is the acceleration of collaboration between data, people and technology.  

“Data is the key resource for AI. Climate data is mostly geospatial. It has a space and a time component. It is massive, massive amounts of data” says Hamann.  The vastness and complexity of data within the Global Climate Network can mean many things, from satellite images, soil moisture readings or altitude of terrain. Typically, data funneled through an AI model is processed in one place or through one process, hampering efficiency. The use of a Global Climate Network, however,  allows participants to discover not just their own climate information, but also information across the network. This method helps accelerate the development of the AI models used, saving time and resources. While each piece of data helps to inform the AI model used by the network, data alone isn’t enough. 

Technology is one of three pillars of the Global Climate Network. Key technologies include the use of hybrid cloud, cloud computing, foundational AI and federated learning, which happens at the edge, like mobile phones, laptops or private servers. And as important as data and the various technologies are in building an intelligence ecosystem, like IBM’s Global Climate Network, Hamann reiterates how that’s synthesized and made actionable.  “Science requires collaboration. There is no progress without science, so collaboration is the key piece.” Equally important are the people behind each enterprise within the network.

People have an important role to play within the network, like locaI and national governments, scientists, physicists, engineers, business enterprises and academia. By leveraging data, technology and different fields of research, these entities can contextualize data into actionable strategies. Valuable insights which may inform public policy or changes to a communities’ infrastructure, all while maintaining the sovereignty of their information. That is the power of an intelligence ecosystem like the Global Climate Network. 

If extreme weather events become more frequent or more hazardous, humans across the globe will need powerful and unique solutions, like actioning IBM’s Global Climate Network. Each flood, while damaging and costly, may provide a lesson for other communities around the globe. The value of this kind of predictive power, means that by understanding the impacts of flooding locally, humans can mitigate against floods and flood impacts globally.  “It’s a distributed network. We have started building a climate network with a few key partners. We have demonstrated discovery of climate information in these different instances and we are eager for others to join and work with us, and collaborate, to find better solutions to climate change.” 

To learn more about the Global Climate Network, visit IBM.com/research today. 

What does it mean to be in bardo? Neither here, nor there, it’s a sort of middle. This concept stems from Tibetan Buddhism, considered a liminal state between death and rebirth that lasts forty-nine days. In this state, consciousness disintegrates as the mind and body split, and one must reconcile all of life’s experiences of past, present and future, into a sort of labyrinth of time and space. This stage is thought to be a state of existence between two lives on earth. And while the concept of limbo isn’t foreign, how is this reflected in the human condition and our identity? 

First, a little about the human condition. Existentialism is a philosophical theory or approach, emphasizing human existence focused through a person’s subjective experiences. As cultural anthropologist Ernest Becker wrote in “The Denial of Death”, the human condition is the “mainspring of human activity.” Essentially, it is the prominent motivator among humans that give shape to our decisions, experiences, and therefore identity.

In Bardo, the protagonist is divided within his own liminal state, reconciling his experiences as a father, a husband, a Mexican, an immigrant and a successful filmmaker and journalist. The film does this through a sort of visual emotional biography, captured on screen in wonderfully shot surrealist dream states. Silverio finds himself in some form of bardo throughout, perhaps most directly, as a Mexican immigrant. His success in his adopted country, the United States, produces shame and guilt in him exacerbated by his yearning for a Mexico he has lost. Silverio’s shame is also a catalyst, promoting an examination of his life which dives into the very storied history between the United States and Mexico.

In 1846, the United States invaded Mexico, a fact highlighted in the film by a battle reenactment, whereby Mexico ceded one thousand three hundred thousand square kilometers. In exchange, Mexico received $15M USD. Philosopher Emilio Uranga considers the fracturing of the Mexican identity as its own form of bardo, a concept  known as Existentialism “a la Mexicana”, or (M)Existentialism.  While similar to its European counterpart, (M)Existentialism is distinguished to include lived circumstance as the differentiating factor, not just subjective experience. Namely cultural, historical and social determinations of Mexican identity, and the acceptance of said circumstance.

Historically, those circumstances began much earlier, with an encounter between the new world and old world. In 1519, Spanish conquistador Hernán Cortés embarked on a violent expedition into Mexico, seeking riches like gold and silver. Traveling from another of Spain’s territories, Cuba, Cortes made advances west eventually arriving at Tenochtitlan, or modern day Mexico City. In that quest, however, the Spaniards and native allies killed thousands of indigenous people, marking the birth of a new, if not traumatic, reality. This collective experience is at the nexus of Mexican identity, the erasure of one culture and history, making room for the next, albeit in this case, as John Charles Chasteen’s book title suggests, “Born in blood and fire”. 

In the film, Silverio engages Cortés, seated atop an enormous heap of indigenous bodies, quoting, ironically, famed Mexican poet Octavio Paz. Cortés claims himself to be the most Mexican, for he was both the first, and therefore the father, of all Mexican people. Despite chastising Cortes, Silverio must reconcile his modern Mexican identity,  and the evolution of that cultural circumstance,  through the historical lens of these traumatic events at the birth of a new experience. Perhaps most significantly, the terms between Mexico and the United States after the war of 1846, when both countries signed the Guadalupe Hidalgo treaty in 1848 in an effort to end the war. Even this marker in Mexico’s history has terms determined by a foreign entity, as Cortés and the Spaniards before. This acknowledgement insists then on a sort of fracturing of Silverio’s identity, as a painful history is now further enmeshed with his yearning of Mexico and his own displacement from it. 

Just as Mexico was fractured in 1846, so too is Silverio, living the Mexican experience from outside of Mexico. Never fully realized as an American, while also suffering a loss of Mexican identity after twenty years away from his homeland. Many immigrants live this type of bardo everyday. If we consider leaving one’s place of origin as a sort of death, then not being fully realized in that adopted place becomes a sort of rebirth, thereby remaining in a sort of middle. Not quite here, not quite there, but in a state of physical, social and cultural bardo.

In the film, Iñárritu compresses all aspects of a life lived— the good, the bad and the ugly, into a sort of labyrinth, a visual design that combines time, space and experiences. A plenitude of themes erupt and coalesce as Silverio seeks a moment of great clarity. Truly, his examination is a reminder for each and every one of us, to seek what lies beneath our own identity. To relive all experiences is to take a sort of karmic audit between experiences like euphoria and suffering, and everything in between. And so the bardo is not solely an experience to be grappled with after death, as we are able to enter it at any moment of our lives. 

What Silverio encounters throughout the film should be a reminder that we can pierce our own identity to find its center, its core, its middle. In reaching this elusive mental and emotional state, we are then able to untether ourselves from ourselves and the identity formed. In this way, as the film suggests, we can each accept the bardo that we truly are. 

As humans are busy living their lives, they’re also producing massive amounts of data, like photos, GPS routes, search queries, data from smart devices, and even shopping habits. So it should come as no surprise that enterprise businesses are overwhelmed trying to wrangle that data, ideally in an efficient manner. The challenge looms larger everyday, as another study found that up to 68% of most organization’s data goes unused. Unused data is essentially untapped potential. All that unused data can leave an enterprise without context for what’s really happening inside their organization, where a data fabric architecture can help to contextualize.

While enterprise businesses continue to accrue data, the sheer volume being produced means those businesses demand a powerful infrastructure to parse through it, migrate it, process it, understand it and learn from it, in order to strategize for the future. That’s where a data fabric can help.

So what is a Data Fabric? No, it’s not clothing for your metaverse avatar. Data Fabric is an architecture design concept. It is a blanket layer that sits across various data sources like cloud systems, on-premises systems or SaaS applications, and integrates these sources through automation. Within that, a data fabric helps to govern who has access to the right information, keeping it secure, and in the correct hands.

Luv Aggarwal, a data platform solution engineer at IBM, says, “Data fabric is not a product. It’s an architecture, it’s a way for an enterprise to set up their data landscape, so no matter what innovation comes out in a couple years, a data fabric will be able to capture that data.” The sheer volume of data projected businesses will have to contend with, warrants a system that also can provide insights for an end user, like a data scientist or an IT leader, for example.

There are three main pillars of a data fabric architecture; the first is access to data wherever it resides. The second pillar is governance, or policies set in place for the data being accessed. And the third pillar is about exposing that data to the folks who need it, using natural language processing.

Consider this simplified scenario: Sheila enters a museum. She grabs a map with information about where to find impressionist art, helping Sheila access the right gallery. As Sheila walks the impressionist gallery, she reads the placards beside each piece of art, with more data about the artist, like the year it was produced and or materials used in the artwork. This space is a gallery, so certain policies apply– so she wouldn’t dare touch the artwork. Lastly, Sheila snaps a photo of the entire gallery, she’s able to assess the influence of one artist on another, as well as the motifs and ideas expressed throughout the style. All of which helps Sheila, the end user, better comprehend the data, or in this case the artwork, and can contextualize everything she’s just seen. Sheila may not be an enterprise, but her experience will help her navigate the art world a little bit better.

Data can be incredibly granular, and using a data fabric allows for a holistic and clearer assessment of any organization. Whether it’s a gallery of impressionist art, or data points of a customer’s streaming habits, a business that sees the big picture, can make better choices within their enterprise.

Taken all together, data has the power to provide clear insights, helping business leaders strategize for the future of their companies. As the data in our world continues to grow, enterprises will need to be prepared with the right tools, like a data fabric, if they’re going to maximize the value of their data. As Luv reminds us that an enterprise “can use that data to make predictions and changes about their experiences today, and having that kind of insight is going to be the real differentiator in the competitive marketplace of tomorrow.”