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In the 19th century, computing pioneer Ada Lovelace wrote that a machine can only do whatever we know how to order it to perform, little knowing that by 2023, AI technology such as chatbot ChatGPT would be holding conversations, solving riddles, and even passing legal and medical exams. The result of this development is eliciting both excitement and concern about the potential implications of these new machines.

The ability of AI to learn from experience is the driving force behind its newfound capabilities. AlphaGo, a program designed to play and improve at the board game Go, defeated its creators using strategies they couldnt explain after playing countless games. Similarly, ChatGPT has processed far more books than any human could ever hope to read.

However, it is essential to understand that intelligence exhibited by machines is not the same as human intelligence. Different species exhibit diverse forms of intelligence without necessarily evolving towards consciousness. For example, the intelligence of AI can recommend a new book to a user, without the need for consciousness.

The obstacles encountered while trying to program machines using human-like language or reasoning led to the development of statistical language models, with the first successful example being crafted by Fredrick Jelinek at IBM. This approach rapidly spread to other areas, leading to data being harvested from the web and focusing AI on observing user behaviour.

While technology has progressed significantly, there are concerns about fair decision-making and the collection of personal data. The delegation of significant decisions to AI systems has also led to tragic outcomes, such as the case of 14-year-old Molly Russell, whose death was partially blamed on harmful algorithms showing her damaging content.

Addressing these problems will require robust legislation to keep pace with AI advancements. A meaningful dialogue on what society expects from AI is essential, drawing input from a diverse range of scholars and grounded in the technical reality of what has been built rather than baseless doomsday scenarios.

Nello Cristianini is a Professor of Artificial Intelligence at the University of Bath. This commentary first appeared on The Conversation, reports Channel News Asia.

Alex is a 42-year-old former corporate executive and business consultant with a degree in business administration. Boasting over 15 years of experience working in various industries, including technology, finance, and marketing, Alex has acquired in-depth knowledge about business strategies, management principles, and market trends. In recent years, Alex has transitioned into writing business articles and providing expert commentary on business-related issues. Fluent in English and proficient in data analysis, Alex strives to deliver well-researched and insightful content to readers, combining practical experience with a keen analytical eye to offer valuable perspectives on the ever-evolving business landscape.

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Machine anxiety: How to reduce confusion and fear about AI technology - Thaiger

Thanks to ChatGPT we can all, finally, experience artificial intelligence. All you need is a web browser, and you can talk directly to the most sophisticated AI system on the planet the crowning achievements of 70 years of effort. And it seems likerealAI the AI we have all seen in the movies. So, does this mean we have finally found the recipe for true AI? Is the end of the road for AI now in sight?

AI is one of humanitys oldest dreams. It goes back at least to classical Greece and the myth of Hephaestus, blacksmith to the gods, who had the power to bring metal creatures to life. Variations on the theme have appeared in myth and fiction ever since then. But it was only with the invention of the computer in the late 1940s that AI began to seem plausible.

Computers are machines that follow instructions. The programs that we give them are nothing more than finely detailed instructions recipes that the computer dutifully follows. Your web browser, your email client, and your word processor all boil down to these incredibly detailed lists of instructions. So, if true AI is possible the dream of having computers that are as capable as humans then it too will amount to such a recipe. All we must do to make AI a reality is find the right recipe. But what might such a recipe look like? And given recent excitement about ChatGPT, GPT-4, and BARD large language models(LLMs), to give them their proper name have we now finally found the recipe for true AI?

For about 40 years, the main idea that drove attempts to build AI was that its recipe would involve modelling the conscious mind the thoughts and reasoning processes that constitute our conscious existence. This approach was called symbolic AI, because our thoughts and reasoning seem to involve languages composed of symbols (letters, words, and punctuation). Symbolic AI involved trying to find recipes that captured these symbolic expressions, as well as recipes to manipulate these symbols to reproduce reasoning and decision making.

Symbolic AI had some successes, but failed spectacularly on a huge range of tasks that seem trivial for humans. Even a task like recognizing a human face was beyond symbolic AI. The reason for this is that recognizing faces is a task that involvesperception.Perception is the problem of understanding what we are seeing, hearing, and sensing. Those of us fortunate enough to have no sensory impairments largely take perception for granted we dont really think about it, and we certainly dont associate it withintelligence.But symbolic AI was just the wrong way of trying to solve problems that require perception.

Instead of modeling themind, an alternative recipe for AI involves modeling structures we see in thebrain.After all, human brains are the only entities that we know of at present that can create human intelligence. If you look at a brain under a microscope, youll see enormous numbers of nerve cells called neurons, connected to one another in vast networks. Each neuron is simply looking for patterns in its network connections. When it recognizes a pattern, it sends signals to its neighbors. Those neighbors in turn are looking for patterns, and when they see one, they communicate with their peers, and so on.

Credit: Daniel Zender / Big Think

Somehow, in ways that we cannot quite explain in any meaningful sense, these enormous networks of neurons can learn, and they ultimately produce intelligent behavior. The field of neural networks (neural nets) originally arose in the 1940s, inspired by the idea that these networks of neurons might be simulated by electrical circuits. Neural networks today are realized in software, rather than in electrical circuits, and to be clear, neural net researchers dont try to actually model the brain, but the software structures they use very large networks of very simple computational devices were inspired by the neural structures we see in brains and nervous systems.

Neural networks have been studied continuously since the 1940s, coming in and out of fashion at various times (notably in the late 1960s and mid 1980s), and often being seen as in competition with symbolic AI. But it is over the past decade that neural networks have decisively started to work. All the hype about AI that we have seen in the past decade is essentially because neural networks started to show rapid progress on a range of AI problems.

Im afraid the reasons why neural nets took off this century are disappointingly mundane. For sure there were scientific advances, like new neural network structures and algorithms for configuring them. But in truth, most of the main ideas behind todays neural networks were known as far back as the 1980s. What this century delivered was lots of data and lots of computing power. Training a neural network requires both, and both became available in abundance this century.

All the headline AI systems we have heard about recently use neural networks. For example, AlphaGo, the famous Go playing program developed by London-based AI company DeepMind, which in March 2016 became the first Go program to beat a world champion player, uses two neural networks, each with 12 neural layers. The data to train the networks came from previous Go games played online, and also from self-play that is, the program playing against itself. The recent headline AI systems ChatGPT and GPT-4 from Microsoft-backed AI company OpenAI, as well as BARD from Google also use neural networks. What makes the recent developments different is simply their scale. Everything about them is on a mind-boggling scale.

Consider the GPT-3 system, announced by OpenAI in the summer of 2020. This is the technology that underpins ChatGPT, and it was the LLM that signaled a breakthrough in this technology. The neural nets that make up GPT-3 are huge. Neural net people talk about the number of parameters in a network to indicate its scale. A parameter in this sense is a network component, either an individual neuron or a connection between neurons. GPT-3 had 175 billion parameters in total; GPT-4 reportedly has 1 trillion. By comparison, a human brain has something like 100 billion neurons in total, connected via as many as 1,000 trillion synaptic connections. Vast though current LLMs are, they are still some way from the scale of the human brain.

The data used to train GPT was 575 gigabytes of text. Maybe you dont think that sounds like a lot after all, you can store that on a regular desktop computer. But this isnt video or photos or music, just ordinary written text. And 575 gigabytes ofordinary written textis an unimaginably large amount far, far more than a person could ever read in a lifetime. Where did they get all this text? Well, for starters, they downloaded the World Wide Web.All of it. Every link in every web page was followed, the text extracted, and then the process repeated, with every link systematically followed until you have every piece of text on the web. English Wikipedia made up just 3% of the total training data.

What about the computer to process all this text and train these vast networks? Computer experts use the term floating point operation or FLOP to refer to an individual arithmetic calculation that is,one FLOP means one act of addition, subtraction, multiplication, or division. Training GPT-3 required 3 x 1023FLOPs. Our ordinary human experiences simply dont equip us to understand numbers that big. Put it this way: If you were to try to train GPT-3 on a typical desktop computer made in 2023, it would need to run continuously for something like10,000 yearsto be able to carry out that many FLOPs.

Of course, OpenAI didnt train GPT-3 on desktop computers. They used very expensive supercomputers containing thousands of specialized AI processors, running for months on end. And that amount of computing is expensive. The computer time required to train GPT-3 would cost millions of dollars on the open market. Apart from anything else, this means that very few organizations can afford to build systems like ChatGPT, apart from a handful of big tech companies and nation-states.

For all their mind-bending scale, LLMs are actually doing something very simple. Suppose you open your smartphone and start a text message to your spouse with the words what time. Your phone will suggestcompletionsof that text for you. It might suggest are you home or is dinner, for example. It suggests these because your phone is predicting that they are the likeliest next words to appear after what time. Your phone makes this prediction based on all the text messages you have sent, and based on these messages, it has learned that these are the likeliest completions of what time. LLMs are doing the same thing, but as we have seen, they do it on a vastly larger scale. The training data is not just your text messages, but all the text available in digital format in the world. What does that scale deliver? Something quite remarkable and unexpected.

Credit: Daniel Zender / Big Think

The first thing we notice when we use ChatGPT or BARD is that they are extremely good at generating very natural text. That is no surprise; its what they are designed to do, and indeed thats the whole point of those 575 gigabytes of text. But the unexpected thing is that, in ways that we dont yet understand, LLMs acquire other capabilities as well: capabilities that must be somehow implicit within the enormous corpus of text they are trained on.

For example, we can ask ChatGPT to summarize a piece of text, and itusually does a creditable job. We can ask it to extract the key points from some text, or compare pieces of text, and it seems pretty good at these tasks as well. Although AI insiders were alerted to the power of LLMs when GPT-3 was released in 2020, the rest of the world only took notice when ChatGPT was released in November 2022. Within a few months, it had attracted hundreds of millions of users. AI has been high-profile for a decade, but the flurry of press and social media coverage when ChatGPT was released was unprecedented: AI went viral.

At this point, there is something I simply must get off my chest. Thanks to ChatGPT, we have finally reached the age of AI. Every day, hundreds of millions of people interact with the most sophisticated AI on the planet. This took 70 years of scientific labor, countless careers, billions upon billions of dollars of investment, hundreds of thousands of scientific papers, and AI supercomputers running at top speed for months. And the AI that the world finally gets isprompt completion.

Right now, the future of trillion-dollar companies is at stake. Their fate depends onprompt completion.Exactly what your mobile phone does. As an AI researcher, working in this field for more than 30 years, I have to say I find this rather galling. Actually, itsoutrageous.Who could possibly have guessed thatthiswould be the version of AI that would finally hit prime time?

Whenever we see a period of rapid progress in AI, someone suggests thatthis is it that we are now on the royal road totrueAI. Given the success of LLMs, it is no surprise that similar claims are being made now. So, lets pause and think about this. If we succeed in AI, then machines should be capable of anything that a human being is capable of.

Consider the two main branches of human intelligence: one involves purely mental capabilities, and the other involves physical capabilities. For example, mental capabilities include logical and abstract reasoning, common sense reasoning (like understanding that dropping an egg on the floor will cause it to break, or understanding that I cant eat Kansas), numeric and mathematical reasoning, problem solving and planning, natural language processing, a rational mental state, a sense of agency, recall, and theory of mind. Physical capabilities include sensory understanding (that is, interpreting the inputs from our five senses), mobility, navigation, manual dexterity and manipulation, hand-eye coordination, and proprioception.

I emphasize that this is far from an exhaustive list of human capabilities. But if we ever havetrueAI AI that is as competent as we are then it will surely have all these capabilities.

The first obvious thing to say is that LLMs are simply not a suitable technology for any of the physical capabilities. LLMs dont exist in the real world at all, and the challenges posed by robotic AI are far, far removed from those that LLMs were designed to address. And in fact, progress on robotic AI has been much more modest than progress on LLMs. Perhaps surprisingly, capabilities like manual dexterity for robots are a long way from being solved. Moreover, LLMs suggest no way forward for those challenges.

Of course, one can easily imagine an AI system that is pure software intellect, so to speak, so how do LLMs shape up when compared to the mental capabilities listed above? Well, of these, the only one that LLMs really can claim to have made very substantial progress on is natural language processing, which means being able to communicate effectively in ordinary human languages. No surprise there; thats what they were designed for.

But their dazzling competence in human-like communication perhaps leads us to believe that they are much more competent at other things than they are. They can do some superficial logical reasoning and problem solving, but it really is superficial at the moment. But perhaps we should be surprised that they can doanythingbeyond natural language processing. They werent designed to do anything else, so anything else is a bonus and any additional capabilities must somehow be implicit in the text that the system was trained on.

For these reasons, and more, it seems unlikely to me that LLM technology alone will provide a route to true AI. LLMs are rather strange, disembodied entities. They dont exist in our world in any real sense and arent aware of it. If you leave an LLM mid-conversation, and go on holiday for a week, it wont wonder where you are. It isnt aware of the passing of time or indeed aware of anything at all. Its a computer program that is literally not doing anything until you type a prompt, and then simply computing a response to that prompt, at which point it again goes back to not doing anything. Their encyclopedic knowledge of the world, such as it is, is frozen at the point they were trained. They dont know of anything after that.

And LLMs have neverexperiencedanything. They are just programs that have ingested unimaginable amounts of text. LLMs might do a great job at describing the sensation of being drunk, but this is only because they have read a lot of descriptions of being drunk. They have not, andcannot,experience it themselves. They have no purpose other than to produce the best response to the prompt you give them.

This doesnt mean they arent impressive (they are) or that they cant be useful (they are). And I truly believe we are at a watershed moment in technology. But lets not confuse these genuine achievements with true AI. LLMs might be one ingredient in the recipe for true AI, but they are surely not the whole recipe and I suspect we dont yet know what some of the other ingredients are.

This article was reprinted with permission ofBig Think, where it wasoriginally published.

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We need more than ChatGPT to have true AI. It is merely the first ingredient in a complex recipe - Freethink

This is WESA Politics, a weekly newsletter by Chris Potter providing analysis about Pittsburgh and state politics.Sign up here to get it every Thursday afternoon.

It was Sara Innamorato and Matt Dugan and Bethany Hallam who appear to have finally buried the previous generation of Democratic leadership in Allegheny County Tuesday night. But it may have been County Treasurer John Weinstein who wrote the old guards epitaph.

There were too many white men running in this race, said Weinstein, seeking to explain how the staunchly progressive Innamorato defeated him and four others in the Democratic primary for county executive.

Those words were widely perceived as a slight to Innamorato, implying as they did that Weinstein and fellow white guys Michael Lamb and Dave Fawcett had been beaten by each other or by some sort of affirmative-action program rather than by her campaigns themes, resources, and ground game. It sounded like the latest in a long line of rationalizations to minimize progressive gains.

But too many white men running has been a criticism of the status quo for years. Weinsteins statement was most notable perhaps because with the last vestiges of their hold on local government swept away the white men themselves seemed to realize it was a problem.

I don't think I would have included that comment in a concession speech, but he's not wrong, said consultant Abigail Gardner. I've been picturing the Spider-Man meme of two or three Spider-Men pointing at each other.

Gardner hasnt had to write a lot of concession speeches lately: She managed Summer Lees successful 2022 bid to become the first Black woman to represent Pennsylvania in Congress, and she been active in progressive politics for years (though she had no formal role in this years campaigns). The reason more established politicians are struggling today, she said, has less to do with them crossing each other than with a failure to connect with anyone else.

Gardner recalled that when she returned to the region in 2015, There were a lot of people who were demoralized by the state of politics locally. There were some strong liberal voices, such as state Rep. Dan Frankel, but newcomers who wanted to change the status quo would be told the powers-that-be just had it on lock and there was no room for anyone else, she said.

But the election of Donald Trump motivated a lot of disenchanted locals to try to make a difference, joining grassroots groups across the county. And the 2018 special election win of Congressman Conor Lamb though far from an ideal candidate from a progressive viewpoint was proof that they could. That race was a crusade that old Democrats and new could join, but when it came to challenging other Democrats, up-and-coming progressives knew how to organize themselves.

The earliest wins, in 2017, elected Anita Prizio to county council and Mik Pappas as a magistrate district judge. Those little-noted campaigns tested themes that would become central to the progressive promise: environmental concerns, criminal justice reforms, housing.

And momentum built from there, with a demographically diverse and ideologically cohesive movement of progressives: Lee and Innamorato came on the scene with successful state House bids in 2018, with Emily Kinkead and Jessica Benham joining them later. Ed Gainey became Mayor, and Lee moved on to Congress. Tuesday night proved conclusively that there is no office outside the movements reach. (Notably, the regions top watchdog posts arent entirely within their grasp: City Controller Rachael Heisler and county Controller Corey OConnor ran on good-government agendas, but they arent explicitly tied to the movement that delivered Innamoratos win.)

On election night, I asked Innamorato how progressives had remade the political landscape so quickly. Youth played a role, she said: Voters age 25-34 make up the countys largest cohort of Democrats they are about 20 percent of the electorate and Their political consciousness is much more seasoned than mine was at their age.

More broadly, she said The name of the game with our campaigns has always been expanding the electorate to reach voters the existing power structure hasnt spoken to. That work involves going out, knocking doors, making phone calls, asking people what their priorities are for their families and their communities, and really connecting that with the polices and the message of our campaign."

Sounds simple, right? Find out who the voters are, listen to what they want, and figure out how to connect it to your candidacy.

But its become clear that, as Gardner puts it, The Democrat machine was really rusted out. At the outset of the county executive race, Weinsteins bid was fueled by unions and others willing to write big checks. But the SEIU service workers union and other advocacy groups in the progressives corner have been much more willing to put their mouths where their money is, supporting door-knocking and other outreach efforts.

In the county executive race, a functioning machine might not have been able to prevent Innamoratos win, but it would have responded to it more coherently. Even before the race began, there would have been an obvious political heir to departing County Executive Rich Fitzgerald or barring that, some ability to clear the field for a contender to rally behind.

As it was, Fitzgerald himself endorsed Lamb at a low-key event just three weeks before the election, though it was clear long before then that Lamb was his only choice. Weinstein didnt have a successor in mind for his old treasurer post at all. And politics abhors a vacuum.

Of course, there are still elections this fall to get through. Steve Zappala may be resuscitated by a successful write-in effort on the Republican ballot, giving him a second chance against progressive Democratic nominee Matt Dugan. Innamorato will face Republican Joe Rockey, and that race will play out like the last two weeks of the Democratic contest did. Youll hear a lot about her lack of executive-branch experience, and about her previous ties to the Democratic Socialists of America, which backed her and Lee in 2018 but whose influence has waned since.

But Tuesday night made official what should have been clear long before: This is a progressive Democratic county now. And you have to realize that if you want to run in a primary or else youre gonna be run into the ground.

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Why Pittsburgh & Allegheny County progressives keep winning elections - 90.5 WESA

Progressive candidates in Pittsburgh won two key races on Tuesday. Reform candidate and chief public defender Matt Dugan ousted a longtime tough-on-crime incumbent to win the primary for district attorney in Pennsylvanias Allegheny County. State Rep. Sara Innamorato won the Democratic primary for Allegheny County executive. Both will advance to general elections in November. Dugan is currently running unopposed, and Innamorato will face Republican candidate Joseph Rockey.

The wins add to a recent body of progressive success in a crucial swing state where Republicans have made inroads in recent years. Since 2018, progressives have surged in Pennsylvania races from Congress to state legislature and local government, picking up key seats in Pittsburgh and Philadelphia along the way.

Conventional politics in swing states have typically shunned progressives in favor of moderate candidates. Tuesdays results are evidence that candidates who prioritize issues facing working people can help build the Democratic base in purple states rather than shrink it, Rep. Summer Lee, a progressive Democrat who went from the Pennsylvania state House to the U.S. House last year, said during remarks at Innamoratos election party on Tuesday night.

Back in the day when they doubted us, and they said, These crazy women cant win those state House seats, we told them back then that the power of the people was greater than the people in power, Lee said. What we showed them tonight, what weve shown them in every single election cycle since weve started is that the power of the people is always greater than the people in power.

In 2018, Innamorato was first elected to the state House along with Lee, who also represented parts of Pittsburgh. They both beat longtime incumbents in Democratic primaries. Philadelphia elected its first Working Families Party council member, Kendra Brooks, in 2019. Philadelphia organizers won election to the state House 2020. And Pittsburgh Mayor Ed Gainey beat a Democratic incumbent and won election in 2021.

In 2022, Lee was elected to the U.S. House of Representatives after defeating a moderate Democrat and an onslaught of outside spending by conservative Democrats and Republicans. Sen. John Fettermans 2022 election over Republican candidate Mehmet Oz built on those progressive wins.

On Tuesday in Philadelphias mayoral race, though, the left candidate lost to Cherelle Parker, a former city council majority leader who had support from a cadre of Democratic officials and local unions, as well as Philadelphias Black clergy. Former city council member Helen Gym came in third place with 21 percent of the vote to Parkers 33 percent. Gyms campaign had been buoyed by endorsements from national progressives, and the last poll in the race showed her with a slight lead. A preliminary breakdown of votes by the Philadelphia Inquirer showed that Parker won in precincts that were majority Black and precincts with incomes below $75,000, while Gym had more support in wealthier and majority white communities. Parker will face Republican candidate and city council member at large David Oh in November.

As candidates focusing on issues of economic inequality, corporate profits, and social infrastructure have surged, theyve faced opposition from both Democrats and Republicans. Republican donors have worked with Democrats to fight insurgent progressive candidates in federal and local races across the country. A Republican megadonor poured out $1.1 million in the final days of the Philadelphia mayoral race to oppose Gym and influence city council races. He is also the sole donor to a new federal PAC launched to target challengers in Democratic primaries.

Preliminary takeaways from Philadelphias mayoral race have compared its dynamics to New York Citys election of Mayor Eric Adams in 2021. Parker campaigned on combating gun violence by ramping up the citys controversial stop-and-frisk policy, which contributed to overpolicing in many Black neighborhoods impacted by crime. Criticisms of the policy fueled a movement for reforms that elected District Attorney Larry Krasner in 2017 and again in 2021. Philadelphians have also endorsed candidates who support more aggressive criminal justice reforms, like city council members Brooks and Jamie Gauthier.

Right-wing media also attacked Dugan for his similarity to Krasner, who campaigned on addressing the citys unequal justice system and holding police accountable for misconduct. Krasner was reelected overwhelmingly in 2021, beating a police-backed opponent who pushed a return to aggressive policing and indiscriminate prosecution. In Allegheny County, Dugan ran a campaign focused on reforming the criminal justice system, diverting low-level offenses, ending cash bail, focusing on violent crime, and enhancing services for victims. Observers have criticized Dugan for his support from the Justice & Public Safety PAC, which is supported by George Soros.

Innamorato ran a campaign similarly focused on housing, environmental equality, investing in mental health treatment and diversion programs, ending solitary confinement, and enforcing corporate taxes. The office of Allegheny County executive is one of the most powerful in the western part of the state and controls a $3 billion annual budget.

Tonight, voters in Allegheny County showed once again that they are hungry for leaders with big ideas who will fight hard for working people, Working Families Party Mid-Atlantic Elections Director Shoshanna Israel said in a statement on Tuesday night. They rejected tough-on-crime fearmongering from a decrepit political establishment and embraced reformers who will invest in and support our communities.

Pennsylvania politics have shifted dramatically in the years since the state voted for former President Donald Trump in 2016. Trumps win energized Pennsylvania Republicans for the first time in decades. That momentum slowed after President Joe Biden flipped the state blue again in 2020, and Republicans have run increasingly extreme candidates as the party seeks to rebuild power. As an antidote, progressive candidates have sought to build coalitions and energize voters who might be new to politics or had grown disenchanted with machine politics.

This is a victory not just for Sara Innamorato and Matt Dugan, Israel continued, but for the people-powered movement that knocked on tens of thousands of doors to elect them. Its a victory for those who want an Allegheny County that works for everyone, not just the powerful and politically connected.

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How Progressives Won and Lost in Purple Pennsylvania - The Intercept

Progressive Democrats and climate hawks are firing warning shots at party leaders and President Joe Biden not to compromise with Republicans on permitting reform as a way to raise the debt ceiling.

In a series of letters, floor speeches and ad campaigns, many on the partys left wing says they will not accept a debt limit deal that includes any undermining of bedrock environmental laws.

If Republicans insist on selling out working Americans or trying to flood America with fossil fuels, then the President must use his constitutional authority to protect America and end this hostage taking, Sen. Jeff Merkley (D-Ore.), tweeted Thursday.

The push has echoes from just a few months ago, when left-leaning lawmakers said they were prepared to shut down the government or delay funding for defense programs rather than vote on a proposal to overhaul the nations energy project permitting laws. Ultimately, they won out, as the permitting effort fizzled.

The stakes this time, however, are far higher. Democrats are not threatening a mere lapse in federal government spending but a default on the nations borrowing authority an event economists say would be cataclysmic.

In debt limit talks between the White House and House Republicans, its not clear what negotiators are eyeing in terms of changes to the permitting process. The White House has confirmed the administration has put it on the table for discussions to avert a default as early as June 1.

Weve been clear we support permitting reform, with Senior Advisor John Podesta outlining our priorities last week, Michael Kikukawa, assistant press secretary, told E&E News in a statement earlier this week.

We have seen bipartisan support for permitting reform and certainly hope there is bipartisan progress. But were not going to detail what negotiators are discussing.

Podesta, a White House climate adviser, is touting an 11-point set of principles the administration would endorse in any permitting package, which includes accelerating grid updates, overhauling outdated mining laws and siting hydrogen and carbon dioxide infrastructure.

Democrats would probably be satisfied with those contours, as they would deal with transmission deployment and benefit clean energy projects rather than oil and gas endeavors.

But Republicans have been pushing for changes to the National Environmental Policy Act (NEPA) to speed up permitting for fossil fuel projects a proposition Democrats largely reject.

And the Republican lawmaker negotiating the terms of a debt ceiling agreement on behalf of House Speaker Kevin McCarthy (R-Calif.) is Rep. Garret Graves (R-La.), the author of the BUILDER Act, the House GOPs opening bid on permitting reform that would streamline environmental reviews with two-year deadlines and limit time for legal challenges to approved permits.

The BUILDER Act was contained in H.R. 1, the Lower Energy Costs Act, which was in turn included in House Republican-passed debt limit bill, H.R. 2811, the Limit, Save, Grow Act.

Under the guise of permitting reform, these extreme, ideological attacks on NEPA would eliminate requirements to consider climate change and pollution impacts, cut public input opportunities, and limit judicial review, more than 60 House Democrats wrote in a letter addressed to Biden and New York Democrats Chuck Schumer, the Senate majority leader, and Hakeem Jeffries, the House minority leader.

The letter, according to a Democratic aide, will be formally transmitted to the three-party leaders in the coming days with the signatures from six committee ranking members.

Among them will be House Natural Resources ranking member Ral Grijalva (D-Ariz.), who is spearheading the opposition now as he did last year, when Biden and Schumer were rallying their members to support a permitting proposal championed by Sen. Joe Manchin in exchange for the West Virginia Democrats vote on the Inflation Reduction Act.

Manchins bill could also be considered in debt limit negotiations, as it would boost transmission deployment as well as oil and gas projects.

It would, similar to the BUILDER Act, set a two-year shot clock on agencies to complete environmental reviews and require legal challenges to be filed within 150 days of a permits issuance.

The Democrats, in their letter, call for four principles to be retained in any debt ceiling discussion that includes permitting:

The Democrats further wrote: We remain deeply concerned that sacrificing any of these four principles will result in serious and detrimental harm to millions of Americans especially those living in low-income communities, Indigenous communities, and communities of color overburdened already by decades of irresponsible industry development.

House Democrats are girding for battle as the League of Conservation Voters and Climate Power prepare to spend an additional $350,000 on a seven-figure ad campaign in states and districts where congressional Republicans are enjoying clean energy manufacturing booms as a result of the Inflation Reduction Act despite voting to repeal the laws clean energy tax credits through the Limit, Save, Grow Act.

In the Senate, some Democrats are channeling their anxiety over a permitting deal that cedes too much to the fossil fuel industry by mobilizing around an effort to compel Biden to invoke the 14th Amendment to raise the debt ceiling.

We also cannot allow these budget negotiations to undermine the historic clean energy and environmental justice investments made by Congress and your administration by allowing fossil fuel companies to unleash a flood of dirty energy projects that will worsen the climate crisis and disproportionately impact frontline communities, 11 senators wrote to Biden on Thursday. We must continue the transition from fossil fuels to clean and renewable energy.

The letter was led by Senate Health, Education, Labor and Pensions Chair Bernie Sanders, a Vermont independent who caucuses with Democrats, and signed by senators including Budget Chair Sheldon Whitehouse (D-R.I.) and another avowed climate hawk, Ed Markey (D-Mass.), who took to the Senate floor on Thursday to air his concerns about where talks might be headed.

Markey, in an interview as a part of POLITICOs energy summit later in the day, accused Republicans of trying to extract a new set of permitting regulations that make it possible for the oil and gas industry to just detonate a carbon bomb over the United States while simultaneously not dealing with the transmission permitting issue.

I suspect thats going on, he said. Im not in the room, but I dont think you have to be a political savant to surmise that this will be the topic of Republican demands.

In a statement overnight, the White House said the president had received a briefing on the talks while traveling abroad. Biden is due back in Washington this weekend.

The President directed his team to continue pressing forward for a bipartisan agreement and made clear the need to protect essential programs for hardworking Americans and the economic progress of the past two years as negotiations head into advanced stages, the statement said.

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Progressives gird for battle as permitting talks escalate - E&E News